[release] v1.3.0: add MSWAL dataset (484 abdominal CT cases, 42 configs)

- new package medvision_ds.datasets.MSWAL — download_raw.py builds from upstream HF zhaodongwu/MSWAL @62c286b0 (484 imagesTr cases; the 210-case test split in dataset.json was never uploaded): header-copy -> uint16 -> RAS+ in the downloader, planner re-split seed 1024 / 0.7 -> 338/146; download_fast.py fetches the preprocessed private mirror YongchengYAO/MSWAL-Lite @39fb50b6
- biometry: 5 fromSeg tasks for labels 3-7 (liver/kidney tumor, pancreatic cancer, liver/kidney cyst); stone labels 1-2 are segmentation/detection-only; all 3 planes survive the single-cluster filter (weakest: liver-cyst sagittal test, 27 slices)
- register 42 configs in MedVision.py (6 Mask-Size + 6 Box-Size + 30 Tumor-Lesion-Size), plus _ANNOTATION_INDEX, _BIOMETRY_FAMILY, DATASETS_NAME2PACKAGE, _VERSION_NOTES, and the release frontier version -> 1.3.0
- add MSWAL's label names to LABEL_MAP_REGROUP so T/L figures use the soft-tissue HU window: "pancreatic cancer" and "liver cyst" were missing (map only knew "pancreas cancer"/"kidney cyst") and fell back to percentile normalization, rendering Label5/6 figures washed-out; "gallstone"/"kidney stone" added too
- Datasets/MSWAL.zip (LFS): Landmarks-Label{3..7}-v1.3.0 + figures (regenerated after the normalization fix) + the three benchmark plans
- bump medvision_ds to 1.3.0; add dataset_specs recipe (download_raw for builds), pyproject package entry, datasets/__init__ imports
- info/v1.3.0 config lists (950 -> 992) and validator expectations (75 pairs, 31 datasets); test_annotation_resolution 440/440, test_tl_ack_gate 16/16
- docs: README catalogue row (HF*, b-box 42/18K = filtered Box-Size rows, T/L 5.8/2.5K), dataset-search survey entry with the overlap-check verdict (11 fingerprint hits, all refuted by voxel comparison), file-structure, changelog

Datasets/MSWAL.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51b55b155c0fcf16bbbab23d4474d0c2fb1bacf30bf008175fafa590ef4437a9
+size 3729385889

MedVision.py

@@ -143,6 +143,7 @@ _ANNOTATION_INDEX = {
     "LNQ2023": {"segmentation": ("1.2.0",), "detection": ("1.2.0",), "biometry": ("1.2.0",)},
     "MAMA-MIA": {"segmentation": ("1.2.1",), "detection": ("1.2.1",), "biometry": ("1.2.1",)},
     "MSD": {"segmentation": ("1.0.0",), "detection": ("1.0.0",), "biometry": ("1.0.0", "1.1.0", "1.1.1")},
+    "MSWAL": {"segmentation": ("1.3.0",), "detection": ("1.3.0",), "biometry": ("1.3.0",)},
     "OAIZIB-CM": {"segmentation": ("1.0.0",), "detection": ("1.0.0",)},
     "PDDCA": {"segmentation": ("1.2.0",), "detection": ("1.2.0",), "biometry": ("1.2.0",)},
     "PI-CAI": {"segmentation": ("1.2.1",), "detection": ("1.2.1",), "biometry": ("1.2.1",)},
@@ -230,6 +231,7 @@ _BIOMETRY_FAMILY = {
     "LNQ2023": "fromSeg",
     "MAMA-MIA": "fromSeg",
     "MSD": "fromSeg",
+    "MSWAL": "fromSeg",
     "PDDCA": "landmark",
     "PI-CAI": "fromSeg",
     "VerSe": "landmark",
@@ -296,6 +298,7 @@ def _acceptable_versions(release_version):
 # version added to the index without a note here is still listed (just bare)
 # rather than silently missing.
 _VERSION_NOTES = {
+    "1.3.0": "adds MSWAL (484 abdominal CT cases, 7-class lesion masks)",
     "1.2.1": "corrects MAMA-MIA and PI-CAI to RAS+ (their v1.2.0 is withdrawn)",
     "1.2.0": "adds 8 datasets (MAMA-MIA and PI-CAI have no 1.2.0 annotation - use 1.2.1)",
     "1.1.1": "fixes transposed in-plane voxel spacing in the TL ellipse fit",
@@ -482,10 +485,11 @@ def _require_planner_version_error(release_version):
         "   loaded at that version; see info/ for the config list of each release.\n"
         "\n"
         " See release notes: \n"
-        "   doc/release-v1.1.0.md\n"
-        "   doc/release-v1.1.1.md\n"
-        "   doc/release-v1.2.0.md\n"
-        "   doc/release-v1.2.1.md\n"
+        "   release-v1.1.0\n"
+        "   release-v1.1.1\n"
+        "   release-v1.2.0\n"
+        "   release-v1.2.1\n"
+        "   @ https://medvision-vlm.github.io/explorer.html\n"
         "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
     )
 
@@ -753,7 +757,7 @@ def _enforce_release_ack(planner_version, latest_version, ack_value=None,
        + ack_block
        + "\n"
         " Release note:\n"
-       f"   doc/release-v{ack_value}.md\n"
+       f"   release-v{ack_value} @ https://medvision-vlm.github.io/explorer.html\n"
         "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
     )
 
@@ -831,7 +835,7 @@ class MedVisionConfig(BuilderConfig):
         self.num_proc = num_proc
 
         super().__init__(
-            version="1.2.1", **kwargs
+            version="1.3.0", **kwargs
         )  # dataset version; keep this hardcoded — MedVision.py is downloaded from
            # the remote repo, so self.config.version must reflect the remote version,
            # not whatever medvision_ds version is currently installed locally.
@@ -10671,6 +10675,433 @@ class MedVision(GeneratorBasedBuilder):
             imageSliceType="sagittal",
             split="test",
         ),
+        # MSWAL:Mask-Size:Task01
+        MedVisionConfig(
+            name="MSWAL_MaskSize_Task01_Sagittal_Train",
+            dataset_name="MSWAL",
+            taskType="Mask-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_MaskSize,
+            imageSliceType="sagittal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_MaskSize_Task01_Sagittal_Test",
+            dataset_name="MSWAL",
+            taskType="Mask-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_MaskSize,
+            imageSliceType="sagittal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_MaskSize_Task01_Coronal_Train",
+            dataset_name="MSWAL",
+            taskType="Mask-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_MaskSize,
+            imageSliceType="coronal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_MaskSize_Task01_Coronal_Test",
+            dataset_name="MSWAL",
+            taskType="Mask-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_MaskSize,
+            imageSliceType="coronal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_MaskSize_Task01_Axial_Train",
+            dataset_name="MSWAL",
+            taskType="Mask-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_MaskSize,
+            imageSliceType="axial",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_MaskSize_Task01_Axial_Test",
+            dataset_name="MSWAL",
+            taskType="Mask-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_MaskSize,
+            imageSliceType="axial",
+            split="test",
+        ),
+        # MSWAL:Box-Size:Task01
+        MedVisionConfig(
+            name="MSWAL_BoxSize_Task01_Sagittal_Train",
+            dataset_name="MSWAL",
+            taskType="Box-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_BoxSize,
+            imageSliceType="sagittal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_BoxSize_Task01_Sagittal_Test",
+            dataset_name="MSWAL",
+            taskType="Box-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_BoxSize,
+            imageSliceType="sagittal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_BoxSize_Task01_Coronal_Train",
+            dataset_name="MSWAL",
+            taskType="Box-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_BoxSize,
+            imageSliceType="coronal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_BoxSize_Task01_Coronal_Test",
+            dataset_name="MSWAL",
+            taskType="Box-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_BoxSize,
+            imageSliceType="coronal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_BoxSize_Task01_Axial_Train",
+            dataset_name="MSWAL",
+            taskType="Box-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_BoxSize,
+            imageSliceType="axial",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_BoxSize_Task01_Axial_Test",
+            dataset_name="MSWAL",
+            taskType="Box-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_BoxSize,
+            imageSliceType="axial",
+            split="test",
+        ),
+        # MSWAL:Tumor-Lesion-Size:Task01
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task01_Sagittal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task01_Sagittal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task01_Coronal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task01_Coronal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task01_Axial_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task01_Axial_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="01",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="test",
+        ),
+        # MSWAL:Tumor-Lesion-Size:Task02
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task02_Sagittal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="02",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task02_Sagittal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="02",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task02_Coronal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="02",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task02_Coronal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="02",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task02_Axial_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="02",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task02_Axial_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="02",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="test",
+        ),
+        # MSWAL:Tumor-Lesion-Size:Task03
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task03_Sagittal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="03",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task03_Sagittal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="03",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task03_Coronal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="03",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task03_Coronal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="03",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task03_Axial_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="03",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task03_Axial_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="03",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="test",
+        ),
+        # MSWAL:Tumor-Lesion-Size:Task04
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task04_Sagittal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="04",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task04_Sagittal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="04",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task04_Coronal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="04",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task04_Coronal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="04",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task04_Axial_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="04",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task04_Axial_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="04",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="test",
+        ),
+        # MSWAL:Tumor-Lesion-Size:Task05
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task05_Sagittal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="05",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task05_Sagittal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="05",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="sagittal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task05_Coronal_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="05",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task05_Coronal_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="05",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="coronal",
+            split="test",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task05_Axial_Train",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="05",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="train",
+        ),
+        MedVisionConfig(
+            name="MSWAL_TumorLesionSize_Task05_Axial_Test",
+            dataset_name="MSWAL",
+            taskType="Tumor-Lesion-Size",
+            taskID="05",
+            imageType="2D",
+            features_dict=features_dict_TumorLesionSize,
+            imageSliceType="axial",
+            split="test",
+        ),
     ]
 
     # Mapping from dataset name to package name
@@ -10709,6 +11140,7 @@ class MedVision(GeneratorBasedBuilder):
         "PDDCA": "PDDCA",
         "PI-CAI": "PICAI",
         "VerSe": "VerSe",
+        "MSWAL": "MSWAL",
     }
 
     def _info(self):

README.md

@@ -51,6 +51,8 @@ MedVision Dataset
 
 
 # News
+- [Aug 9, 2026] 🚀 Release **MedVision** dataset v1.3.0 [[release-v1.3.0]](https://huggingface.co/datasets/YongchengYAO/MedVision/blob/main/doc/release-v1.3.0.md)
+  - New dataset: MSWAL (484 abdominal CT cases; tumor/lesion labels: liver tumour, kidney tumour, pancreatic cancer, liver cyst, and kidney cyst).
 
 - [Aug 3, 2026] 🚀 Release **MedVision** dataset v1.2.1 [[release-v1.2.1]](https://huggingface.co/datasets/YongchengYAO/MedVision/blob/main/doc/release-v1.2.1.md)
   - ⚠️ **Corrects MAMA-MIA and PI-CAI, whose v1.2.0 annotations were recorded in the source orientation** instead of RAS+ — the loader reoriented the images at load time without renumbering the coordinates. Their v1.2.0 annotations are **withdrawn**. If you have used either dataset, [clear that cache once](https://huggingface.co/datasets/YongchengYAO/MedVision/blob/main/doc/release-v1.2.1.md#do-i-need-to-do-anything).
@@ -92,7 +94,8 @@ For essential updates, check the [change log](https://huggingface.co/datasets/Yo
       b-box: bounding box; T/L: tumor/lesion size; A/D: angle/distance; HF:
       HuggingFace; GC: Grand-Challenge; * redistributed. Sample counts are for
       annotation **v1.2.0** — b-box and A/D are identical in every release, and
-      only T/L was ever regenerated (in 1.1.0 and 1.1.1).
+      only T/L was ever regenerated (in 1.1.0 and 1.1.1). MSWAL was introduced
+      at **v1.3.0**; its counts are for that release.
 
 | **Dataset**      | **Anatomy**   | **Modality** | **Annotation** | **Availability** | **Source**     | **# Sample (Train/Test)** |              |                | **Status** |
 | ---------------- | ------------- | ------------ | -------------- | ---------------- | -------------- | ------------------------- | ------------ | -------------- | ---------- |
@@ -119,6 +122,7 @@ For essential updates, check the [change log](https://huggingface.co/datasets/Yo
 | LNQ2023          | mediastinum   | CT           | b-box, T/L     | open             | HF*, TCIA      | 1.2 / 0.5K                | 34 / 11      | 0              | ✅          |
 | MAMA-MIA         | breast        | MRI          | b-box, T/L     | open             | HF*, Synapse   | 47 / 21K                  | 2.3 / 1.0K   | 0              | ✅          |
 | MSD              | multiple      | CT, MRI      | b-box, T/L     | open             | HF*, others    | 0.2 / 0.1M                | 4.3 / 1.8K   | 0              | ✅          |
+| MSWAL            | abdomen       | CT           | b-box, T/L     | open             | HF*            | 42 / 18K                  | 5.8 / 2.5K   | 0              | ✅          |
 | OAIZIB-CM        | knee          | MRI          | b-box          | open             | HF             | 0.5 / 0.2M                | 0            | 0              | ✅          |
 | PDDCA            | head and neck | CT           | b-box, A/D     | open             | HF*, others    | 10 / 4.8K                 | 0            | 92 / 40        | ✅          |
 | PI-CAI           | prostate      | MRI          | b-box, T/L     | open             | HF*, Zenodo    | 3.9 / 1.6K                | 238 / 157    | 0              | ✅          |
@@ -127,7 +131,7 @@ For essential updates, check the [change log](https://huggingface.co/datasets/Yo
 | TopCoW24         | brain         | CT, MRI      | b-box          | open             | HF*, Zenodo    | 29 / 13K                  | 0            | 0              | ✅          |
 | TotalSegmentator | multiple      | CT, MRI      | b-box          | open             | HF*, Zenodo    | 5.4 / 2.2M                | 0            | 0              | ✅          |
 | VerSe            | spine         | CT           | b-box, A/D     | open             | HF*, others    | 0.2 / 0.1M                | 0            | 1.1 / 0.5K     | ✅          |
-| **Total**        |               |              |                |                  |                | **17 / 7.3M**             | **28 / 12K** | **7.0 / 3.0K** |            |
+| **Total**        |               |              |                |                  |                | **17 / 7.3M**             | **34 / 15K** | **7.0 / 3.0K** |            |
 
 ⚠️ For the following datasets, which do not allow redistribution, you need to apply for access from data owners, (optionally) upload to your private HF dataset repo, and set corresponding environment variables.
 

doc/changelog.md

@@ -2,6 +2,7 @@
 
 This is a summary of essential changes.
 
+- [Aug, 2026] [src] add **MSWAL** dataset (unreleased — ships with v1.3.0): 484 abdominal CT cases (MICCAI 2025, newly collected single-hospital data), 7-class lesion masks; 42 configs — Mask-Size + Box-Size (Task01) and Tumor-Lesion-Size Task01–05 for liver tumor, kidney tumor, pancreatic cancer, liver cyst, kidney cyst (stone labels are segmentation/detection-only), all three planes; catalogue 950 → 992
 - [Aug, 2026] [release] release **MedVision dataset v1.2.1**
   - [fix] **MAMA-MIA and PI-CAI annotations were recorded in the source orientation**, not RAS+ — neither `download_raw.py` reoriented and no preprocessing run passed `--reorient2RAS`, so the planner wrote coordinates in the `('L','A','I')` / `('P','S','L')` / `('L','P','S')` source frames while `MedVision.py` step 3.3 reoriented the images to RAS+ at load time without renumbering the `*.json.gz`. Both downloaders now reorient before any annotation is computed, and both datasets were regenerated as v1.2.1; their v1.2.0 is **withdrawn** — deleted from the hub and de-listed from `_ANNOTATION_INDEX`, so a `1.2.0` pin now reports those 36 configs as not published at that version. The `_PAUSED_ANNOTATIONS` gate that withheld them while the correction was prepared is retained but empty, for the next incident. Train/test splits and all segmentation/detection entry counts are unchanged; 65,195/65,195 detection boxes reproduce from the reoriented masks. See `doc/release-v1.2.1.md`
   - [fix] **annotation values no longer depend on the installed numpy version** — recorded sizes were `int * float32`, which NEP 50 (numpy 2.0) stopped widening to `float64`, so the same code wrote `16.5` where numpy 1.26 wrote `16.49999976158142`. The spacing is now cast with `float()`, pinning the result to the value every published annotation already contains. Also casts `bool` → `int32` before `find_objects`, which `scipy >= 1.15` rejects

doc/dataset-search/dataset-candidates.md

@@ -70,6 +70,29 @@ Two traps found during integration that are worth carrying forward:
 
 ---
 
+## Added in v1.3.0 (1)
+
+| Dataset | Anatomy / Modality | Cases | Tasks | Licence | Why it earned a place |
+| --- | --- | ---: | --- | --- | --- |
+| **MSWAL** | abdomen / CT | 484 | Mask, Box, T/L | CC BY-NC 4.0 | MICCAI 2025, 7-class whole-abdominal-lesion masks (gallstone, kidney stone, liver/kidney tumor, pancreatic cancer, liver/kidney cyst) on newly collected single-hospital CT — first release, so zero split-correctness risk against the shipped catalogue. |
+
+Integration notes (surveyed and added 2026-08-08):
+
+- **Provenance / overlap:** paper (arXiv 2503.13560) states all 694 volumes are newly collected at
+  one hospital and released for the first time. `check_dataset_overlap.py` reported 11/484 exact
+  `(shape, spacing)` fingerprint hits — all refuted by direct voxel comparison (central body-patch
+  correlation ≈ 0 for the strongest pair, and one LIDC chest scan "matching" three distinct MSWAL
+  patients). They are collisions on standard GE recon grids (0.703/0.742 mm × 1.25 mm), not shared
+  scans.
+- **Upstream test split is vapor.** `dataset.json` declares 484 train + 210 test, but the 210
+  `imagesTs/` files were never uploaded to HF (`zhaodongwu/MSWAL`, pinned `62c286b0`). Only the 484
+  `imagesTr` cases exist; the planner re-splits them (seed 1024, 0.7 → 338/146). If the authors
+  ever publish the test set, that is a NEW dataset version.
+- **Stones are segmentation/detection-only by decision:** labels 1–2 (gallstone, kidney stone) are
+  tiny, routinely multi-instance findings; T/L biometry covers labels 3–7 only (5 tasks). All
+  three planes survive the single-cluster filter for all five targets (weakest cell: liver-cyst
+  sagittal test, 27 slices — above the LNQ2023 shipped floor of 11).
+
 ## Deep-dived, then dropped (6)
 
 These reached a build-ready spec and were still not shipped. Each entry is a decision, not an

doc/file-structure.md

@@ -191,6 +191,20 @@
         ├── Images
         ├── Masks
       ├── benchmark_plan_*.json.gz
+    ├── MSWAL
+      ├── Images
+      ├── Landmarks-Label3
+      ├── Landmarks-Label3-fig
+      ├── Landmarks-Label4
+      ├── Landmarks-Label4-fig
+      ├── Landmarks-Label5
+      ├── Landmarks-Label5-fig
+      ├── Landmarks-Label6
+      ├── Landmarks-Label6-fig
+      ├── Landmarks-Label7
+      ├── Landmarks-Label7-fig
+      ├── Masks
+      ├── benchmark_plan_*.json.gz
     ├── OAIZIB-CM
       ├── Images
       ├── Masks

doc/release-v1.3.0.md

@@ -0,0 +1,73 @@
+# Release v1.3.0
+
+**v1.3.0 adds MSWAL: 484 abdominal CT cases with 42 MedVision configurations.** The release provides segmentation and detection plans for gallstones and kidney stones, plus biometry plans for five tumour and lesion labels. It also fixes CT-window selection for several newly introduced label names, so their tumour/lesion figures render in the intended soft-tissue window.
+
+```bash
+export MedVision_PLANNER_VERSION=latest   # resolves to 1.3.0
+```
+
+## Summary
+
+| | Change | In one line | Action |
+| --- | --- | --- | --- |
+| **Major** | | | |
+| 1 | [MSWAL dataset](#mswal-dataset) | 484 abdominal CT cases, 42 configurations, and five biometry tasks | update to `1.3.0` or `latest` to use it |
+| 2 | [Reproducible download paths](#download-paths) | build from the upstream source or use the pinned preprocessed mirror | none |
+| **Minor** | | | |
+| 3 | [CT figure normalization](#ct-figure-normalization) | new cancer, cyst, and stone labels use the intended CT windows | none |
+| 4 | [Catalogue and validation updates](#catalogue-and-validation) | configuration catalogue grows from 950 to 992 entries | none |
+
+## MSWAL dataset
+
+MSWAL contributes **484 abdominal CT cases**. The upstream `dataset.json` names a 210-case test split, but those cases were not uploaded; MedVision therefore plans a reproducible split of the available cohort using seed `1024` and a `0.7` training ratio:
+
+| Split | Cases |
+| --- | ---: |
+| Train | 338 |
+| Test | 146 |
+| Total | 484 |
+
+The dataset adds 42 configurations:
+
+| Family | Configurations | Scope |
+| --- | ---: | --- |
+| Mask-Size | 6 | segmentation size benchmarks |
+| Box-Size | 6 | detection size benchmarks |
+| Tumor-Lesion-Size | 30 | five labels across three anatomical planes |
+| **Total** | **42** | |
+
+The five biometry labels are liver tumour, kidney tumour, pancreatic cancer, liver cyst, and kidney cyst (labels 3–7). Gallstone and kidney stone (labels 1–2) are included for segmentation and detection only.
+
+## Download paths
+
+MSWAL has two supported preparation routes:
+
+| Route | Source | Pinned revision | Notes |
+| --- | --- | --- | --- |
+| Raw build | `zhaodongwu/MSWAL` | `62c286b0` | `download_raw.py` copies headers, converts images to `uint16`, and reorients to RAS+ before planning |
+| Fast download | `YongchengYAO/MSWAL-Lite` | `39fb50b6` | `download_fast.py` retrieves the prepared images and masks |
+
+The raw route uses the 484 uploaded `imagesTr` cases and applies the split above. Reorienting to RAS+ during download ensures image arrays and generated annotations share the same coordinate frame.
+
+## CT figure normalization
+
+`LABEL_MAP_REGROUP` now recognizes `pancreatic cancer`, `liver cyst`, `gallstone`, and `kidney stone`. Therefore, we can use the intended soft-tissue Hounsfield-unit window for image normalization.
+
+## Catalogue and validation
+
+The release registers MSWAL in `MedVision.py`, including its annotation index, biometry family, package mapping, and version notes. The package version and release frontier are now `1.3.0`.
+
+The published catalogue now contains **992 configurations**, up from 950. Validator expectations were updated to 75 annotation-resolution pairs across 31 datasets. The release was checked with:
+
+- `test_annotation_resolution`: 440 / 440 checks passed
+- `test_tl_ack_gate`: 16 / 16 checks passed
+
+## For maintainers
+
+The MSWAL build recipe is registered in `scripts/gen-annotations/dataset_specs.py`; use the raw route when rebuilding the dataset from source. The package is exposed through `medvision_ds.datasets`, and the preprocessed archive, benchmark plans, landmarks, and regenerated figures are published in `Datasets/MSWAL.zip`.
+
+## See also
+
+- `doc/changelog.md` — release history
+- `doc/file-structure.md` — repository layout
+- `scripts/gen-annotations/README.md` — rebuilding dataset annotations

info/v1.3.0/ConfigurationsList_All.csv

@@ -0,0 +1,992 @@
+AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Train
+AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Test
+AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Train
+AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Test
+AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Train
+AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Test
+AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Train
+AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Test
+AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Train
+AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Test
+AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Train
+AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Test
+AbdomenCT-1K_MaskSize_Task01_Sagittal_Train
+AbdomenCT-1K_MaskSize_Task01_Sagittal_Test
+AbdomenCT-1K_MaskSize_Task01_Coronal_Train
+AbdomenCT-1K_MaskSize_Task01_Coronal_Test
+AbdomenCT-1K_MaskSize_Task01_Axial_Train
+AbdomenCT-1K_MaskSize_Task01_Axial_Test
+AbdomenCT-1K_BoxSize_Task01_Sagittal_Train
+AbdomenCT-1K_BoxSize_Task01_Sagittal_Test
+AbdomenCT-1K_BoxSize_Task01_Coronal_Train
+AbdomenCT-1K_BoxSize_Task01_Coronal_Test
+AbdomenCT-1K_BoxSize_Task01_Axial_Train
+AbdomenCT-1K_BoxSize_Task01_Axial_Test
+ACDC_MaskSize_Task01_Sagittal_Train
+ACDC_MaskSize_Task01_Sagittal_Test
+ACDC_MaskSize_Task01_Coronal_Train
+ACDC_MaskSize_Task01_Coronal_Test
+ACDC_MaskSize_Task01_Axial_Train
+ACDC_MaskSize_Task01_Axial_Test
+ACDC_BoxSize_Task01_Sagittal_Train
+ACDC_BoxSize_Task01_Sagittal_Test
+ACDC_BoxSize_Task01_Coronal_Train
+ACDC_BoxSize_Task01_Coronal_Test
+ACDC_BoxSize_Task01_Axial_Train
+ACDC_BoxSize_Task01_Axial_Test
+AMOS22_MaskSize_Task01_Sagittal_Train
+AMOS22_MaskSize_Task01_Sagittal_Test
+AMOS22_MaskSize_Task01_Coronal_Train
+AMOS22_MaskSize_Task01_Coronal_Test
+AMOS22_MaskSize_Task01_Axial_Train
+AMOS22_MaskSize_Task01_Axial_Test
+AMOS22_MaskSize_Task02_Sagittal_Train
+AMOS22_MaskSize_Task02_Sagittal_Test
+AMOS22_MaskSize_Task02_Coronal_Train
+AMOS22_MaskSize_Task02_Coronal_Test
+AMOS22_MaskSize_Task02_Axial_Train
+AMOS22_MaskSize_Task02_Axial_Test
+AMOS22_BoxSize_Task01_Sagittal_Train
+AMOS22_BoxSize_Task01_Sagittal_Test
+AMOS22_BoxSize_Task01_Coronal_Train
+AMOS22_BoxSize_Task01_Coronal_Test
+AMOS22_BoxSize_Task01_Axial_Train
+AMOS22_BoxSize_Task01_Axial_Test
+AMOS22_BoxSize_Task02_Sagittal_Train
+AMOS22_BoxSize_Task02_Sagittal_Test
+AMOS22_BoxSize_Task02_Coronal_Train
+AMOS22_BoxSize_Task02_Coronal_Test
+AMOS22_BoxSize_Task02_Axial_Train
+AMOS22_BoxSize_Task02_Axial_Test
+autoPET-III_MaskSize_Task01_Sagittal_Train
+autoPET-III_MaskSize_Task01_Sagittal_Test
+autoPET-III_MaskSize_Task01_Coronal_Train
+autoPET-III_MaskSize_Task01_Coronal_Test
+autoPET-III_MaskSize_Task01_Axial_Train
+autoPET-III_MaskSize_Task01_Axial_Test
+autoPET-III_MaskSize_Task02_Sagittal_Train
+autoPET-III_MaskSize_Task02_Sagittal_Test
+autoPET-III_MaskSize_Task02_Coronal_Train
+autoPET-III_MaskSize_Task02_Coronal_Test
+autoPET-III_MaskSize_Task02_Axial_Train
+autoPET-III_MaskSize_Task02_Axial_Test
+autoPET-III_BoxSize_Task01_Sagittal_Train
+autoPET-III_BoxSize_Task01_Sagittal_Test
+autoPET-III_BoxSize_Task01_Coronal_Train
+autoPET-III_BoxSize_Task01_Coronal_Test
+autoPET-III_BoxSize_Task01_Axial_Train
+autoPET-III_BoxSize_Task01_Axial_Test
+autoPET-III_BoxSize_Task02_Sagittal_Train
+autoPET-III_BoxSize_Task02_Sagittal_Test
+autoPET-III_BoxSize_Task02_Coronal_Train
+autoPET-III_BoxSize_Task02_Coronal_Test
+autoPET-III_BoxSize_Task02_Axial_Train
+autoPET-III_BoxSize_Task02_Axial_Test
+autoPET-III_TumorLesionSize_Task01_Sagittal_Train
+autoPET-III_TumorLesionSize_Task01_Sagittal_Test
+autoPET-III_TumorLesionSize_Task01_Coronal_Train
+autoPET-III_TumorLesionSize_Task01_Coronal_Test
+autoPET-III_TumorLesionSize_Task01_Axial_Train
+autoPET-III_TumorLesionSize_Task01_Axial_Test
+BCV15_MaskSize_Task01_Sagittal_Train
+BCV15_MaskSize_Task01_Sagittal_Test
+BCV15_MaskSize_Task01_Coronal_Train
+BCV15_MaskSize_Task01_Coronal_Test
+BCV15_MaskSize_Task01_Axial_Train
+BCV15_MaskSize_Task01_Axial_Test
+BCV15_MaskSize_Task02_Sagittal_Train
+BCV15_MaskSize_Task02_Sagittal_Test
+BCV15_MaskSize_Task02_Coronal_Train
+BCV15_MaskSize_Task02_Coronal_Test
+BCV15_MaskSize_Task02_Axial_Train
+BCV15_MaskSize_Task02_Axial_Test
+BCV15_BoxSize_Task01_Sagittal_Train
+BCV15_BoxSize_Task01_Sagittal_Test
+BCV15_BoxSize_Task01_Coronal_Train
+BCV15_BoxSize_Task01_Coronal_Test
+BCV15_BoxSize_Task01_Axial_Train
+BCV15_BoxSize_Task01_Axial_Test
+BCV15_BoxSize_Task02_Sagittal_Train
+BCV15_BoxSize_Task02_Sagittal_Test
+BCV15_BoxSize_Task02_Coronal_Train
+BCV15_BoxSize_Task02_Coronal_Test
+BCV15_BoxSize_Task02_Axial_Train
+BCV15_BoxSize_Task02_Axial_Test
+BraTS24_MaskSize_Task01_Sagittal_Train
+BraTS24_MaskSize_Task01_Sagittal_Test
+BraTS24_MaskSize_Task01_Coronal_Train
+BraTS24_MaskSize_Task01_Coronal_Test
+BraTS24_MaskSize_Task01_Axial_Train
+BraTS24_MaskSize_Task01_Axial_Test
+BraTS24_MaskSize_Task02_Sagittal_Train
+BraTS24_MaskSize_Task02_Sagittal_Test
+BraTS24_MaskSize_Task02_Coronal_Train
+BraTS24_MaskSize_Task02_Coronal_Test
+BraTS24_MaskSize_Task02_Axial_Train
+BraTS24_MaskSize_Task02_Axial_Test
+BraTS24_MaskSize_Task03_Sagittal_Train
+BraTS24_MaskSize_Task03_Sagittal_Test
+BraTS24_MaskSize_Task03_Coronal_Train
+BraTS24_MaskSize_Task03_Coronal_Test
+BraTS24_MaskSize_Task03_Axial_Train
+BraTS24_MaskSize_Task03_Axial_Test
+BraTS24_MaskSize_Task04_Sagittal_Train
+BraTS24_MaskSize_Task04_Sagittal_Test
+BraTS24_MaskSize_Task04_Coronal_Train
+BraTS24_MaskSize_Task04_Coronal_Test
+BraTS24_MaskSize_Task04_Axial_Train
+BraTS24_MaskSize_Task04_Axial_Test
+BraTS24_MaskSize_Task05_Sagittal_Train
+BraTS24_MaskSize_Task05_Sagittal_Test
+BraTS24_MaskSize_Task05_Coronal_Train
+BraTS24_MaskSize_Task05_Coronal_Test
+BraTS24_MaskSize_Task05_Axial_Train
+BraTS24_MaskSize_Task05_Axial_Test
+BraTS24_MaskSize_Task06_Sagittal_Train
+BraTS24_MaskSize_Task06_Sagittal_Test
+BraTS24_MaskSize_Task06_Coronal_Train
+BraTS24_MaskSize_Task06_Coronal_Test
+BraTS24_MaskSize_Task06_Axial_Train
+BraTS24_MaskSize_Task06_Axial_Test
+BraTS24_MaskSize_Task07_Sagittal_Train
+BraTS24_MaskSize_Task07_Sagittal_Test
+BraTS24_MaskSize_Task07_Coronal_Train
+BraTS24_MaskSize_Task07_Coronal_Test
+BraTS24_MaskSize_Task07_Axial_Train
+BraTS24_MaskSize_Task07_Axial_Test
+BraTS24_MaskSize_Task08_Sagittal_Train
+BraTS24_MaskSize_Task08_Sagittal_Test
+BraTS24_MaskSize_Task08_Coronal_Train
+BraTS24_MaskSize_Task08_Coronal_Test
+BraTS24_MaskSize_Task08_Axial_Train
+BraTS24_MaskSize_Task08_Axial_Test
+BraTS24_MaskSize_Task09_Sagittal_Train
+BraTS24_MaskSize_Task09_Sagittal_Test
+BraTS24_MaskSize_Task09_Coronal_Train
+BraTS24_MaskSize_Task09_Coronal_Test
+BraTS24_MaskSize_Task09_Axial_Train
+BraTS24_MaskSize_Task09_Axial_Test
+BraTS24_MaskSize_Task10_Sagittal_Train
+BraTS24_MaskSize_Task10_Sagittal_Test
+BraTS24_MaskSize_Task10_Coronal_Train
+BraTS24_MaskSize_Task10_Coronal_Test
+BraTS24_MaskSize_Task10_Axial_Train
+BraTS24_MaskSize_Task10_Axial_Test
+BraTS24_MaskSize_Task11_Sagittal_Train
+BraTS24_MaskSize_Task11_Sagittal_Test
+BraTS24_MaskSize_Task11_Coronal_Train
+BraTS24_MaskSize_Task11_Coronal_Test
+BraTS24_MaskSize_Task11_Axial_Train
+BraTS24_MaskSize_Task11_Axial_Test
+BraTS24_MaskSize_Task12_Sagittal_Train
+BraTS24_MaskSize_Task12_Sagittal_Test
+BraTS24_MaskSize_Task12_Coronal_Train
+BraTS24_MaskSize_Task12_Coronal_Test
+BraTS24_MaskSize_Task12_Axial_Train
+BraTS24_MaskSize_Task12_Axial_Test
+BraTS24_MaskSize_Task13_Sagittal_Train
+BraTS24_MaskSize_Task13_Sagittal_Test
+BraTS24_MaskSize_Task13_Coronal_Train
+BraTS24_MaskSize_Task13_Coronal_Test
+BraTS24_MaskSize_Task13_Axial_Train
+BraTS24_MaskSize_Task13_Axial_Test
+BraTS24_BoxSize_Task01_Sagittal_Train
+BraTS24_BoxSize_Task01_Sagittal_Test
+BraTS24_BoxSize_Task01_Coronal_Train
+BraTS24_BoxSize_Task01_Coronal_Test
+BraTS24_BoxSize_Task01_Axial_Train
+BraTS24_BoxSize_Task01_Axial_Test
+BraTS24_BoxSize_Task02_Sagittal_Train
+BraTS24_BoxSize_Task02_Sagittal_Test
+BraTS24_BoxSize_Task02_Coronal_Train
+BraTS24_BoxSize_Task02_Coronal_Test
+BraTS24_BoxSize_Task02_Axial_Train
+BraTS24_BoxSize_Task02_Axial_Test
+BraTS24_BoxSize_Task03_Sagittal_Train
+BraTS24_BoxSize_Task03_Sagittal_Test
+BraTS24_BoxSize_Task03_Coronal_Train
+BraTS24_BoxSize_Task03_Coronal_Test
+BraTS24_BoxSize_Task03_Axial_Train
+BraTS24_BoxSize_Task03_Axial_Test
+BraTS24_BoxSize_Task04_Sagittal_Train
+BraTS24_BoxSize_Task04_Sagittal_Test
+BraTS24_BoxSize_Task04_Coronal_Train
+BraTS24_BoxSize_Task04_Coronal_Test
+BraTS24_BoxSize_Task04_Axial_Train
+BraTS24_BoxSize_Task04_Axial_Test
+BraTS24_BoxSize_Task05_Sagittal_Train
+BraTS24_BoxSize_Task05_Sagittal_Test
+BraTS24_BoxSize_Task05_Coronal_Train
+BraTS24_BoxSize_Task05_Coronal_Test
+BraTS24_BoxSize_Task05_Axial_Train
+BraTS24_BoxSize_Task05_Axial_Test
+BraTS24_BoxSize_Task06_Sagittal_Train
+BraTS24_BoxSize_Task06_Sagittal_Test
+BraTS24_BoxSize_Task06_Coronal_Train
+BraTS24_BoxSize_Task06_Coronal_Test
+BraTS24_BoxSize_Task06_Axial_Train
+BraTS24_BoxSize_Task06_Axial_Test
+BraTS24_BoxSize_Task07_Sagittal_Train
+BraTS24_BoxSize_Task07_Sagittal_Test
+BraTS24_BoxSize_Task07_Coronal_Train
+BraTS24_BoxSize_Task07_Coronal_Test
+BraTS24_BoxSize_Task07_Axial_Train
+BraTS24_BoxSize_Task07_Axial_Test
+BraTS24_BoxSize_Task08_Sagittal_Train
+BraTS24_BoxSize_Task08_Sagittal_Test
+BraTS24_BoxSize_Task08_Coronal_Train
+BraTS24_BoxSize_Task08_Coronal_Test
+BraTS24_BoxSize_Task08_Axial_Train
+BraTS24_BoxSize_Task08_Axial_Test
+BraTS24_BoxSize_Task09_Sagittal_Train
+BraTS24_BoxSize_Task09_Sagittal_Test
+BraTS24_BoxSize_Task09_Coronal_Train
+BraTS24_BoxSize_Task09_Coronal_Test
+BraTS24_BoxSize_Task09_Axial_Train
+BraTS24_BoxSize_Task09_Axial_Test
+BraTS24_BoxSize_Task10_Sagittal_Train
+BraTS24_BoxSize_Task10_Sagittal_Test
+BraTS24_BoxSize_Task10_Coronal_Train
+BraTS24_BoxSize_Task10_Coronal_Test
+BraTS24_BoxSize_Task10_Axial_Train
+BraTS24_BoxSize_Task10_Axial_Test
+BraTS24_BoxSize_Task11_Sagittal_Train
+BraTS24_BoxSize_Task11_Sagittal_Test
+BraTS24_BoxSize_Task11_Coronal_Train
+BraTS24_BoxSize_Task11_Coronal_Test
+BraTS24_BoxSize_Task11_Axial_Train
+BraTS24_BoxSize_Task11_Axial_Test
+BraTS24_BoxSize_Task12_Sagittal_Train
+BraTS24_BoxSize_Task12_Sagittal_Test
+BraTS24_BoxSize_Task12_Coronal_Train
+BraTS24_BoxSize_Task12_Coronal_Test
+BraTS24_BoxSize_Task12_Axial_Train
+BraTS24_BoxSize_Task12_Axial_Test
+BraTS24_BoxSize_Task13_Sagittal_Train
+BraTS24_BoxSize_Task13_Sagittal_Test
+BraTS24_BoxSize_Task13_Coronal_Train
+BraTS24_BoxSize_Task13_Coronal_Test
+BraTS24_BoxSize_Task13_Axial_Train
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+MAMA-MIA_BoxSize_Task01_Sagittal_Train
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+MSWAL_TumorLesionSize_Task05_Axial_Train
+MSWAL_TumorLesionSize_Task05_Axial_Test

info/v1.3.0/ConfigurationsList_Test.csv

@@ -0,0 +1,496 @@
+AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Test
+AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Test
+AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Test
+AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Test
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+AbdomenCT-1K_MaskSize_Task01_Sagittal_Test
+AbdomenCT-1K_MaskSize_Task01_Coronal_Test
+AbdomenCT-1K_MaskSize_Task01_Axial_Test
+AbdomenCT-1K_BoxSize_Task01_Sagittal_Test
+AbdomenCT-1K_BoxSize_Task01_Coronal_Test
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+ACDC_MaskSize_Task01_Sagittal_Test
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+ACDC_MaskSize_Task01_Axial_Test
+ACDC_BoxSize_Task01_Sagittal_Test
+ACDC_BoxSize_Task01_Coronal_Test
+ACDC_BoxSize_Task01_Axial_Test
+AMOS22_MaskSize_Task01_Sagittal_Test
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+AMOS22_BoxSize_Task01_Sagittal_Test
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+AMOS22_BoxSize_Task02_Sagittal_Test
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+autoPET-III_MaskSize_Task01_Sagittal_Test
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+autoPET-III_BoxSize_Task01_Sagittal_Test
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+BraTS24_BoxSize_Task13_Sagittal_Test
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+BraTS24_TumorLesionSize_Task06_Axial_Test
+BraTS24_TumorLesionSize_Task07_Sagittal_Test
+BraTS24_TumorLesionSize_Task07_Coronal_Test
+BraTS24_TumorLesionSize_Task07_Axial_Test
+BraTS24_TumorLesionSize_Task08_Sagittal_Test
+BraTS24_TumorLesionSize_Task08_Coronal_Test
+BraTS24_TumorLesionSize_Task08_Axial_Test
+BraTS24_TumorLesionSize_Task09_Sagittal_Test
+BraTS24_TumorLesionSize_Task09_Coronal_Test
+BraTS24_TumorLesionSize_Task09_Axial_Test
+BraTS24_TumorLesionSize_Task10_Sagittal_Test
+BraTS24_TumorLesionSize_Task10_Coronal_Test
+BraTS24_TumorLesionSize_Task10_Axial_Test
+BraTS24_TumorLesionSize_Task11_Sagittal_Test
+BraTS24_TumorLesionSize_Task11_Coronal_Test
+BraTS24_TumorLesionSize_Task11_Axial_Test
+BraTS24_TumorLesionSize_Task12_Sagittal_Test
+BraTS24_TumorLesionSize_Task12_Coronal_Test
+BraTS24_TumorLesionSize_Task12_Axial_Test
+CAMUS_MaskSize_Task01_Sagittal_Test
+CAMUS_MaskSize_Task01_Coronal_Test
+CAMUS_MaskSize_Task01_Axial_Test
+CAMUS_BoxSize_Task01_Sagittal_Test
+CAMUS_BoxSize_Task01_Coronal_Test
+CAMUS_BoxSize_Task01_Axial_Test
+Ceph-Biometrics-400_BiometricsFromLandmarks_Distance_Task01_Sagittal_Test
+Ceph-Biometrics-400_BiometricsFromLandmarks_Angle_Task01_Sagittal_Test
+CrossMoDA_MaskSize_Task01_Sagittal_Test
+CrossMoDA_MaskSize_Task01_Coronal_Test
+CrossMoDA_MaskSize_Task01_Axial_Test
+CrossMoDA_BoxSize_Task01_Sagittal_Test
+CrossMoDA_BoxSize_Task01_Coronal_Test
+CrossMoDA_BoxSize_Task01_Axial_Test
+FeTA24_MaskSize_Task01_Sagittal_Test
+FeTA24_MaskSize_Task01_Coronal_Test
+FeTA24_MaskSize_Task01_Axial_Test
+FeTA24_BoxSize_Task01_Sagittal_Test
+FeTA24_BoxSize_Task01_Coronal_Test
+FeTA24_BoxSize_Task01_Axial_Test
+FeTA24_BiometricsFromLandmarks_Task01_Sagittal_Test
+FeTA24_BiometricsFromLandmarks_Task01_Coronal_Test
+FeTA24_BiometricsFromLandmarks_Task01_Axial_Test
+FLARE22_MaskSize_Task01_Sagittal_Test
+FLARE22_MaskSize_Task01_Coronal_Test
+FLARE22_MaskSize_Task01_Axial_Test
+FLARE22_BoxSize_Task01_Sagittal_Test
+FLARE22_BoxSize_Task01_Coronal_Test
+FLARE22_BoxSize_Task01_Axial_Test
+HNTSMRG24_MaskSize_Task01_Sagittal_Test
+HNTSMRG24_MaskSize_Task01_Coronal_Test
+HNTSMRG24_MaskSize_Task01_Axial_Test
+HNTSMRG24_MaskSize_Task02_Sagittal_Test
+HNTSMRG24_MaskSize_Task02_Coronal_Test
+HNTSMRG24_MaskSize_Task02_Axial_Test
+HNTSMRG24_BoxSize_Task01_Sagittal_Test
+HNTSMRG24_BoxSize_Task01_Coronal_Test
+HNTSMRG24_BoxSize_Task01_Axial_Test
+HNTSMRG24_BoxSize_Task02_Sagittal_Test
+HNTSMRG24_BoxSize_Task02_Coronal_Test
+HNTSMRG24_BoxSize_Task02_Axial_Test
+HNTSMRG24_TumorLesionSize_Task01_Sagittal_Test
+HNTSMRG24_TumorLesionSize_Task01_Coronal_Test
+HNTSMRG24_TumorLesionSize_Task01_Axial_Test
+HNTSMRG24_TumorLesionSize_Task02_Sagittal_Test
+HNTSMRG24_TumorLesionSize_Task02_Coronal_Test
+HNTSMRG24_TumorLesionSize_Task02_Axial_Test
+HNTSMRG24_TumorLesionSize_Task03_Sagittal_Test
+HNTSMRG24_TumorLesionSize_Task03_Coronal_Test
+HNTSMRG24_TumorLesionSize_Task03_Axial_Test
+HNTSMRG24_TumorLesionSize_Task04_Sagittal_Test
+HNTSMRG24_TumorLesionSize_Task04_Coronal_Test
+HNTSMRG24_TumorLesionSize_Task04_Axial_Test
+ISLES24_MaskSize_Task01_Sagittal_Test
+ISLES24_MaskSize_Task01_Coronal_Test
+ISLES24_MaskSize_Task01_Axial_Test
+ISLES24_MaskSize_Task02_Sagittal_Test
+ISLES24_MaskSize_Task02_Coronal_Test
+ISLES24_MaskSize_Task02_Axial_Test
+ISLES24_BoxSize_Task01_Sagittal_Test
+ISLES24_BoxSize_Task01_Coronal_Test
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+ISLES24_BoxSize_Task02_Sagittal_Test
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+ISLES24_BoxSize_Task02_Axial_Test
+KiPA22_MaskSize_Task01_Sagittal_Test
+KiPA22_MaskSize_Task01_Coronal_Test
+KiPA22_MaskSize_Task01_Axial_Test
+KiPA22_BoxSize_Task01_Sagittal_Test
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+KiPA22_BoxSize_Task01_Axial_Test
+KiPA22_TumorLesionSize_Task01_Sagittal_Test
+KiPA22_TumorLesionSize_Task01_Coronal_Test
+KiPA22_TumorLesionSize_Task01_Axial_Test
+KiTS23_MaskSize_Task01_Sagittal_Test
+KiTS23_MaskSize_Task01_Coronal_Test
+KiTS23_MaskSize_Task01_Axial_Test
+KiTS23_BoxSize_Task01_Sagittal_Test
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+KiTS23_BoxSize_Task01_Axial_Test
+KiTS23_TumorLesionSize_Task01_Sagittal_Test
+KiTS23_TumorLesionSize_Task01_Coronal_Test
+KiTS23_TumorLesionSize_Task01_Axial_Test
+MSD_MaskSize_Task01_Sagittal_Test
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+MSD_MaskSize_Task02_Sagittal_Test
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+MSD_MaskSize_Task03_Sagittal_Test
+MSD_MaskSize_Task03_Coronal_Test
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+MSD_MaskSize_Task04_Sagittal_Test
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+MSD_MaskSize_Task05_Sagittal_Test
+MSD_MaskSize_Task05_Coronal_Test
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+MSD_MaskSize_Task06_Sagittal_Test
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+MSD_MaskSize_Task07_Sagittal_Test
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+MSD_MaskSize_Task08_Sagittal_Test
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+MSD_MaskSize_Task09_Sagittal_Test
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+MSD_MaskSize_Task10_Sagittal_Test
+MSD_MaskSize_Task10_Coronal_Test
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+MSD_MaskSize_Task11_Sagittal_Test
+MSD_MaskSize_Task11_Coronal_Test
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+MSD_MaskSize_Task12_Sagittal_Test
+MSD_MaskSize_Task12_Coronal_Test
+MSD_MaskSize_Task12_Axial_Test
+MSD_MaskSize_Task13_Sagittal_Test
+MSD_MaskSize_Task13_Coronal_Test
+MSD_MaskSize_Task13_Axial_Test
+MSD_MaskSize_Task14_Sagittal_Test
+MSD_MaskSize_Task14_Coronal_Test
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+MSD_BoxSize_Task01_Sagittal_Test
+MSD_BoxSize_Task01_Coronal_Test
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+MSD_BoxSize_Task02_Sagittal_Test
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+MSD_BoxSize_Task03_Sagittal_Test
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+MSD_BoxSize_Task04_Sagittal_Test
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+MSD_BoxSize_Task05_Coronal_Test
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+MSD_BoxSize_Task06_Sagittal_Test
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+MSD_BoxSize_Task07_Sagittal_Test
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+MSD_BoxSize_Task08_Sagittal_Test
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+MSD_BoxSize_Task09_Sagittal_Test
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+MSD_BoxSize_Task10_Sagittal_Test
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+MSD_BoxSize_Task11_Sagittal_Test
+MSD_BoxSize_Task11_Coronal_Test
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+MSD_BoxSize_Task12_Sagittal_Test
+MSD_BoxSize_Task12_Coronal_Test
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+MSD_BoxSize_Task13_Sagittal_Test
+MSD_BoxSize_Task13_Coronal_Test
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+MSD_BoxSize_Task14_Sagittal_Test
+MSD_BoxSize_Task14_Coronal_Test
+MSD_BoxSize_Task14_Axial_Test
+MSD_TumorLesionSize_Task01_Sagittal_Test
+MSD_TumorLesionSize_Task01_Coronal_Test
+MSD_TumorLesionSize_Task01_Axial_Test
+MSD_TumorLesionSize_Task02_Sagittal_Test
+MSD_TumorLesionSize_Task02_Coronal_Test
+MSD_TumorLesionSize_Task02_Axial_Test
+MSD_TumorLesionSize_Task03_Sagittal_Test
+MSD_TumorLesionSize_Task03_Coronal_Test
+MSD_TumorLesionSize_Task03_Axial_Test
+MSD_TumorLesionSize_Task04_Sagittal_Test
+MSD_TumorLesionSize_Task04_Coronal_Test
+MSD_TumorLesionSize_Task04_Axial_Test
+MSD_TumorLesionSize_Task05_Sagittal_Test
+MSD_TumorLesionSize_Task05_Coronal_Test
+MSD_TumorLesionSize_Task05_Axial_Test
+MSD_TumorLesionSize_Task06_Sagittal_Test
+MSD_TumorLesionSize_Task06_Coronal_Test
+MSD_TumorLesionSize_Task06_Axial_Test
+MSD_TumorLesionSize_Task07_Sagittal_Test
+MSD_TumorLesionSize_Task07_Coronal_Test
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+MSD_TumorLesionSize_Task08_Sagittal_Test
+MSD_TumorLesionSize_Task08_Coronal_Test
+MSD_TumorLesionSize_Task08_Axial_Test
+OAIZIB-CM_MaskSize_Task01_Sagittal_Test
+OAIZIB-CM_MaskSize_Task01_Coronal_Test
+OAIZIB-CM_MaskSize_Task01_Axial_Test
+OAIZIB-CM_BoxSize_Task01_Sagittal_Test
+OAIZIB-CM_BoxSize_Task01_Coronal_Test
+OAIZIB-CM_BoxSize_Task01_Axial_Test
+SKM-TEA_MaskSize_Task01_Sagittal_Test
+SKM-TEA_MaskSize_Task01_Coronal_Test
+SKM-TEA_MaskSize_Task01_Axial_Test
+SKM-TEA_MaskSize_Task02_Sagittal_Test
+SKM-TEA_MaskSize_Task02_Coronal_Test
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+SKM-TEA_BoxSize_Task02_Sagittal_Test
+SKM-TEA_BoxSize_Task02_Coronal_Test
+SKM-TEA_BoxSize_Task02_Axial_Test
+ToothFairy2_MaskSize_Task01_Sagittal_Test
+ToothFairy2_MaskSize_Task01_Coronal_Test
+ToothFairy2_MaskSize_Task01_Axial_Test
+ToothFairy2_BoxSize_Task01_Sagittal_Test
+ToothFairy2_BoxSize_Task01_Coronal_Test
+ToothFairy2_BoxSize_Task01_Axial_Test
+TopCoW24_MaskSize_Task01_Sagittal_Test
+TopCoW24_MaskSize_Task01_Coronal_Test
+TopCoW24_MaskSize_Task01_Axial_Test
+TopCoW24_MaskSize_Task02_Sagittal_Test
+TopCoW24_MaskSize_Task02_Coronal_Test
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+TopCoW24_BoxSize_Task01_Sagittal_Test
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+TopCoW24_BoxSize_Task02_Sagittal_Test
+TopCoW24_BoxSize_Task02_Coronal_Test
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+TotalSegmentator_MaskSize_Task01_Sagittal_Test
+TotalSegmentator_MaskSize_Task01_Coronal_Test
+TotalSegmentator_MaskSize_Task01_Axial_Test
+TotalSegmentator_MaskSize_Task02_Sagittal_Test
+TotalSegmentator_MaskSize_Task02_Coronal_Test
+TotalSegmentator_MaskSize_Task02_Axial_Test
+TotalSegmentator_BoxSize_Task01_Sagittal_Test
+TotalSegmentator_BoxSize_Task01_Coronal_Test
+TotalSegmentator_BoxSize_Task01_Axial_Test
+TotalSegmentator_BoxSize_Task02_Sagittal_Test
+TotalSegmentator_BoxSize_Task02_Coronal_Test
+TotalSegmentator_BoxSize_Task02_Axial_Test
+AFIDs_BiometricsFromLandmarks_Task01_Sagittal_Test
+AFIDs_BiometricsFromLandmarks_Task01_Axial_Test
+DEEP-PSMA_MaskSize_Task01_Sagittal_Test
+DEEP-PSMA_MaskSize_Task01_Coronal_Test
+DEEP-PSMA_MaskSize_Task01_Axial_Test
+DEEP-PSMA_MaskSize_Task02_Sagittal_Test
+DEEP-PSMA_MaskSize_Task02_Coronal_Test
+DEEP-PSMA_MaskSize_Task02_Axial_Test
+DEEP-PSMA_BoxSize_Task01_Sagittal_Test
+DEEP-PSMA_BoxSize_Task01_Coronal_Test
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+DEEP-PSMA_BoxSize_Task02_Sagittal_Test
+DEEP-PSMA_BoxSize_Task02_Coronal_Test
+DEEP-PSMA_BoxSize_Task02_Axial_Test
+DEEP-PSMA_TumorLesionSize_Task01_Axial_Test
+DEEP-PSMA_TumorLesionSize_Task02_Axial_Test
+LIDC-IDRI_BoxSize_Task01_Sagittal_Test
+LIDC-IDRI_BoxSize_Task01_Coronal_Test
+LIDC-IDRI_BoxSize_Task01_Axial_Test
+LIDC-IDRI_MaskSize_Task01_Sagittal_Test
+LIDC-IDRI_MaskSize_Task01_Coronal_Test
+LIDC-IDRI_MaskSize_Task01_Axial_Test
+LIDC-IDRI_TumorLesionSize_Task01_Sagittal_Test
+LIDC-IDRI_TumorLesionSize_Task01_Coronal_Test
+LIDC-IDRI_TumorLesionSize_Task01_Axial_Test
+LNQ2023_BoxSize_Task01_Sagittal_Test
+LNQ2023_BoxSize_Task01_Coronal_Test
+LNQ2023_BoxSize_Task01_Axial_Test
+LNQ2023_MaskSize_Task01_Sagittal_Test
+LNQ2023_MaskSize_Task01_Coronal_Test
+LNQ2023_MaskSize_Task01_Axial_Test
+LNQ2023_TumorLesionSize_Task01_Axial_Test
+MAMA-MIA_BoxSize_Task01_Sagittal_Test
+MAMA-MIA_BoxSize_Task01_Coronal_Test
+MAMA-MIA_BoxSize_Task01_Axial_Test
+MAMA-MIA_MaskSize_Task01_Sagittal_Test
+MAMA-MIA_MaskSize_Task01_Coronal_Test
+MAMA-MIA_MaskSize_Task01_Axial_Test
+MAMA-MIA_TumorLesionSize_Task01_Sagittal_Test
+MAMA-MIA_TumorLesionSize_Task01_Coronal_Test
+MAMA-MIA_TumorLesionSize_Task01_Axial_Test
+PDDCA_MaskSize_Task01_Sagittal_Test
+PDDCA_MaskSize_Task01_Coronal_Test
+PDDCA_MaskSize_Task01_Axial_Test
+PDDCA_BoxSize_Task01_Sagittal_Test
+PDDCA_BoxSize_Task01_Coronal_Test
+PDDCA_BoxSize_Task01_Axial_Test
+PDDCA_BiometricsFromLandmarks_Task01_Sagittal_Test
+PDDCA_BiometricsFromLandmarks_Task01_Axial_Test
+PI-CAI_BoxSize_Task01_Sagittal_Test
+PI-CAI_BoxSize_Task01_Coronal_Test
+PI-CAI_BoxSize_Task01_Axial_Test
+PI-CAI_MaskSize_Task01_Sagittal_Test
+PI-CAI_MaskSize_Task01_Coronal_Test
+PI-CAI_MaskSize_Task01_Axial_Test
+PI-CAI_TumorLesionSize_Task01_Sagittal_Test
+PI-CAI_TumorLesionSize_Task01_Coronal_Test
+PI-CAI_TumorLesionSize_Task01_Axial_Test
+VerSe_MaskSize_Task01_Sagittal_Test
+VerSe_MaskSize_Task01_Coronal_Test
+VerSe_MaskSize_Task01_Axial_Test
+VerSe_BoxSize_Task01_Sagittal_Test
+VerSe_BoxSize_Task01_Coronal_Test
+VerSe_BoxSize_Task01_Axial_Test
+VerSe_BiometricsFromLandmarks_Task01_Sagittal_Test
+MSWAL_MaskSize_Task01_Sagittal_Test
+MSWAL_MaskSize_Task01_Coronal_Test
+MSWAL_MaskSize_Task01_Axial_Test
+MSWAL_BoxSize_Task01_Sagittal_Test
+MSWAL_BoxSize_Task01_Coronal_Test
+MSWAL_BoxSize_Task01_Axial_Test
+MSWAL_TumorLesionSize_Task01_Sagittal_Test
+MSWAL_TumorLesionSize_Task01_Coronal_Test
+MSWAL_TumorLesionSize_Task01_Axial_Test
+MSWAL_TumorLesionSize_Task02_Sagittal_Test
+MSWAL_TumorLesionSize_Task02_Coronal_Test
+MSWAL_TumorLesionSize_Task02_Axial_Test
+MSWAL_TumorLesionSize_Task03_Sagittal_Test
+MSWAL_TumorLesionSize_Task03_Coronal_Test
+MSWAL_TumorLesionSize_Task03_Axial_Test
+MSWAL_TumorLesionSize_Task04_Sagittal_Test
+MSWAL_TumorLesionSize_Task04_Coronal_Test
+MSWAL_TumorLesionSize_Task04_Axial_Test
+MSWAL_TumorLesionSize_Task05_Sagittal_Test
+MSWAL_TumorLesionSize_Task05_Coronal_Test
+MSWAL_TumorLesionSize_Task05_Axial_Test

info/v1.3.0/ConfigurationsList_Train.csv

@@ -0,0 +1,496 @@
+AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Train
+AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Train
+AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Train
+AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Train
+AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Train
+AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Train
+AbdomenCT-1K_MaskSize_Task01_Sagittal_Train
+AbdomenCT-1K_MaskSize_Task01_Coronal_Train
+AbdomenCT-1K_MaskSize_Task01_Axial_Train
+AbdomenCT-1K_BoxSize_Task01_Sagittal_Train
+AbdomenCT-1K_BoxSize_Task01_Coronal_Train
+AbdomenCT-1K_BoxSize_Task01_Axial_Train
+ACDC_MaskSize_Task01_Sagittal_Train
+ACDC_MaskSize_Task01_Coronal_Train
+ACDC_MaskSize_Task01_Axial_Train
+ACDC_BoxSize_Task01_Sagittal_Train
+ACDC_BoxSize_Task01_Coronal_Train
+ACDC_BoxSize_Task01_Axial_Train
+AMOS22_MaskSize_Task01_Sagittal_Train
+AMOS22_MaskSize_Task01_Coronal_Train
+AMOS22_MaskSize_Task01_Axial_Train
+AMOS22_MaskSize_Task02_Sagittal_Train
+AMOS22_MaskSize_Task02_Coronal_Train
+AMOS22_MaskSize_Task02_Axial_Train
+AMOS22_BoxSize_Task01_Sagittal_Train
+AMOS22_BoxSize_Task01_Coronal_Train
+AMOS22_BoxSize_Task01_Axial_Train
+AMOS22_BoxSize_Task02_Sagittal_Train
+AMOS22_BoxSize_Task02_Coronal_Train
+AMOS22_BoxSize_Task02_Axial_Train
+autoPET-III_MaskSize_Task01_Sagittal_Train
+autoPET-III_MaskSize_Task01_Coronal_Train
+autoPET-III_MaskSize_Task01_Axial_Train
+autoPET-III_MaskSize_Task02_Sagittal_Train
+autoPET-III_MaskSize_Task02_Coronal_Train
+autoPET-III_MaskSize_Task02_Axial_Train
+autoPET-III_BoxSize_Task01_Sagittal_Train
+autoPET-III_BoxSize_Task01_Coronal_Train
+autoPET-III_BoxSize_Task01_Axial_Train
+autoPET-III_BoxSize_Task02_Sagittal_Train
+autoPET-III_BoxSize_Task02_Coronal_Train
+autoPET-III_BoxSize_Task02_Axial_Train
+autoPET-III_TumorLesionSize_Task01_Sagittal_Train
+autoPET-III_TumorLesionSize_Task01_Coronal_Train
+autoPET-III_TumorLesionSize_Task01_Axial_Train
+BCV15_MaskSize_Task01_Sagittal_Train
+BCV15_MaskSize_Task01_Coronal_Train
+BCV15_MaskSize_Task01_Axial_Train
+BCV15_MaskSize_Task02_Sagittal_Train
+BCV15_MaskSize_Task02_Coronal_Train
+BCV15_MaskSize_Task02_Axial_Train
+BCV15_BoxSize_Task01_Sagittal_Train
+BCV15_BoxSize_Task01_Coronal_Train
+BCV15_BoxSize_Task01_Axial_Train
+BCV15_BoxSize_Task02_Sagittal_Train
+BCV15_BoxSize_Task02_Coronal_Train
+BCV15_BoxSize_Task02_Axial_Train
+BraTS24_MaskSize_Task01_Sagittal_Train
+BraTS24_MaskSize_Task01_Coronal_Train
+BraTS24_MaskSize_Task01_Axial_Train
+BraTS24_MaskSize_Task02_Sagittal_Train
+BraTS24_MaskSize_Task02_Coronal_Train
+BraTS24_MaskSize_Task02_Axial_Train
+BraTS24_MaskSize_Task03_Sagittal_Train
+BraTS24_MaskSize_Task03_Coronal_Train
+BraTS24_MaskSize_Task03_Axial_Train
+BraTS24_MaskSize_Task04_Sagittal_Train
+BraTS24_MaskSize_Task04_Coronal_Train
+BraTS24_MaskSize_Task04_Axial_Train
+BraTS24_MaskSize_Task05_Sagittal_Train
+BraTS24_MaskSize_Task05_Coronal_Train
+BraTS24_MaskSize_Task05_Axial_Train
+BraTS24_MaskSize_Task06_Sagittal_Train
+BraTS24_MaskSize_Task06_Coronal_Train
+BraTS24_MaskSize_Task06_Axial_Train
+BraTS24_MaskSize_Task07_Sagittal_Train
+BraTS24_MaskSize_Task07_Coronal_Train
+BraTS24_MaskSize_Task07_Axial_Train
+BraTS24_MaskSize_Task08_Sagittal_Train
+BraTS24_MaskSize_Task08_Coronal_Train
+BraTS24_MaskSize_Task08_Axial_Train
+BraTS24_MaskSize_Task09_Sagittal_Train
+BraTS24_MaskSize_Task09_Coronal_Train
+BraTS24_MaskSize_Task09_Axial_Train
+BraTS24_MaskSize_Task10_Sagittal_Train
+BraTS24_MaskSize_Task10_Coronal_Train
+BraTS24_MaskSize_Task10_Axial_Train
+BraTS24_MaskSize_Task11_Sagittal_Train
+BraTS24_MaskSize_Task11_Coronal_Train
+BraTS24_MaskSize_Task11_Axial_Train
+BraTS24_MaskSize_Task12_Sagittal_Train
+BraTS24_MaskSize_Task12_Coronal_Train
+BraTS24_MaskSize_Task12_Axial_Train
+BraTS24_MaskSize_Task13_Sagittal_Train
+BraTS24_MaskSize_Task13_Coronal_Train
+BraTS24_MaskSize_Task13_Axial_Train
+BraTS24_BoxSize_Task01_Sagittal_Train
+BraTS24_BoxSize_Task01_Coronal_Train
+BraTS24_BoxSize_Task01_Axial_Train
+BraTS24_BoxSize_Task02_Sagittal_Train
+BraTS24_BoxSize_Task02_Coronal_Train
+BraTS24_BoxSize_Task02_Axial_Train
+BraTS24_BoxSize_Task03_Sagittal_Train
+BraTS24_BoxSize_Task03_Coronal_Train
+BraTS24_BoxSize_Task03_Axial_Train
+BraTS24_BoxSize_Task04_Sagittal_Train
+BraTS24_BoxSize_Task04_Coronal_Train
+BraTS24_BoxSize_Task04_Axial_Train
+BraTS24_BoxSize_Task05_Sagittal_Train
+BraTS24_BoxSize_Task05_Coronal_Train
+BraTS24_BoxSize_Task05_Axial_Train
+BraTS24_BoxSize_Task06_Sagittal_Train
+BraTS24_BoxSize_Task06_Coronal_Train
+BraTS24_BoxSize_Task06_Axial_Train
+BraTS24_BoxSize_Task07_Sagittal_Train
+BraTS24_BoxSize_Task07_Coronal_Train
+BraTS24_BoxSize_Task07_Axial_Train
+BraTS24_BoxSize_Task08_Sagittal_Train
+BraTS24_BoxSize_Task08_Coronal_Train
+BraTS24_BoxSize_Task08_Axial_Train
+BraTS24_BoxSize_Task09_Sagittal_Train
+BraTS24_BoxSize_Task09_Coronal_Train
+BraTS24_BoxSize_Task09_Axial_Train
+BraTS24_BoxSize_Task10_Sagittal_Train
+BraTS24_BoxSize_Task10_Coronal_Train
+BraTS24_BoxSize_Task10_Axial_Train
+BraTS24_BoxSize_Task11_Sagittal_Train
+BraTS24_BoxSize_Task11_Coronal_Train
+BraTS24_BoxSize_Task11_Axial_Train
+BraTS24_BoxSize_Task12_Sagittal_Train
+BraTS24_BoxSize_Task12_Coronal_Train
+BraTS24_BoxSize_Task12_Axial_Train
+BraTS24_BoxSize_Task13_Sagittal_Train
+BraTS24_BoxSize_Task13_Coronal_Train
+BraTS24_BoxSize_Task13_Axial_Train
+BraTS24_TumorLesionSize_Task01_Sagittal_Train
+BraTS24_TumorLesionSize_Task01_Coronal_Train
+BraTS24_TumorLesionSize_Task01_Axial_Train
+BraTS24_TumorLesionSize_Task02_Sagittal_Train
+BraTS24_TumorLesionSize_Task02_Coronal_Train
+BraTS24_TumorLesionSize_Task02_Axial_Train
+BraTS24_TumorLesionSize_Task03_Sagittal_Train
+BraTS24_TumorLesionSize_Task03_Coronal_Train
+BraTS24_TumorLesionSize_Task03_Axial_Train
+BraTS24_TumorLesionSize_Task04_Sagittal_Train
+BraTS24_TumorLesionSize_Task04_Coronal_Train
+BraTS24_TumorLesionSize_Task04_Axial_Train
+BraTS24_TumorLesionSize_Task05_Sagittal_Train
+BraTS24_TumorLesionSize_Task05_Coronal_Train
+BraTS24_TumorLesionSize_Task05_Axial_Train
+BraTS24_TumorLesionSize_Task06_Sagittal_Train
+BraTS24_TumorLesionSize_Task06_Coronal_Train
+BraTS24_TumorLesionSize_Task06_Axial_Train
+BraTS24_TumorLesionSize_Task07_Sagittal_Train
+BraTS24_TumorLesionSize_Task07_Coronal_Train
+BraTS24_TumorLesionSize_Task07_Axial_Train
+BraTS24_TumorLesionSize_Task08_Sagittal_Train
+BraTS24_TumorLesionSize_Task08_Coronal_Train
+BraTS24_TumorLesionSize_Task08_Axial_Train
+BraTS24_TumorLesionSize_Task09_Sagittal_Train
+BraTS24_TumorLesionSize_Task09_Coronal_Train
+BraTS24_TumorLesionSize_Task09_Axial_Train
+BraTS24_TumorLesionSize_Task10_Sagittal_Train
+BraTS24_TumorLesionSize_Task10_Coronal_Train
+BraTS24_TumorLesionSize_Task10_Axial_Train
+BraTS24_TumorLesionSize_Task11_Sagittal_Train
+BraTS24_TumorLesionSize_Task11_Coronal_Train
+BraTS24_TumorLesionSize_Task11_Axial_Train
+BraTS24_TumorLesionSize_Task12_Sagittal_Train
+BraTS24_TumorLesionSize_Task12_Coronal_Train
+BraTS24_TumorLesionSize_Task12_Axial_Train
+CAMUS_MaskSize_Task01_Sagittal_Train
+CAMUS_MaskSize_Task01_Coronal_Train
+CAMUS_MaskSize_Task01_Axial_Train
+CAMUS_BoxSize_Task01_Sagittal_Train
+CAMUS_BoxSize_Task01_Coronal_Train
+CAMUS_BoxSize_Task01_Axial_Train
+Ceph-Biometrics-400_BiometricsFromLandmarks_Distance_Task01_Sagittal_Train
+Ceph-Biometrics-400_BiometricsFromLandmarks_Angle_Task01_Sagittal_Train
+CrossMoDA_MaskSize_Task01_Sagittal_Train
+CrossMoDA_MaskSize_Task01_Coronal_Train
+CrossMoDA_MaskSize_Task01_Axial_Train
+CrossMoDA_BoxSize_Task01_Sagittal_Train
+CrossMoDA_BoxSize_Task01_Coronal_Train
+CrossMoDA_BoxSize_Task01_Axial_Train
+FeTA24_MaskSize_Task01_Sagittal_Train
+FeTA24_MaskSize_Task01_Coronal_Train
+FeTA24_MaskSize_Task01_Axial_Train
+FeTA24_BoxSize_Task01_Sagittal_Train
+FeTA24_BoxSize_Task01_Coronal_Train
+FeTA24_BoxSize_Task01_Axial_Train
+FeTA24_BiometricsFromLandmarks_Task01_Sagittal_Train
+FeTA24_BiometricsFromLandmarks_Task01_Coronal_Train
+FeTA24_BiometricsFromLandmarks_Task01_Axial_Train
+FLARE22_MaskSize_Task01_Sagittal_Train
+FLARE22_MaskSize_Task01_Coronal_Train
+FLARE22_MaskSize_Task01_Axial_Train
+FLARE22_BoxSize_Task01_Sagittal_Train
+FLARE22_BoxSize_Task01_Coronal_Train
+FLARE22_BoxSize_Task01_Axial_Train
+HNTSMRG24_MaskSize_Task01_Sagittal_Train
+HNTSMRG24_MaskSize_Task01_Coronal_Train
+HNTSMRG24_MaskSize_Task01_Axial_Train
+HNTSMRG24_MaskSize_Task02_Sagittal_Train
+HNTSMRG24_MaskSize_Task02_Coronal_Train
+HNTSMRG24_MaskSize_Task02_Axial_Train
+HNTSMRG24_BoxSize_Task01_Sagittal_Train
+HNTSMRG24_BoxSize_Task01_Coronal_Train
+HNTSMRG24_BoxSize_Task01_Axial_Train
+HNTSMRG24_BoxSize_Task02_Sagittal_Train
+HNTSMRG24_BoxSize_Task02_Coronal_Train
+HNTSMRG24_BoxSize_Task02_Axial_Train
+HNTSMRG24_TumorLesionSize_Task01_Sagittal_Train
+HNTSMRG24_TumorLesionSize_Task01_Coronal_Train
+HNTSMRG24_TumorLesionSize_Task01_Axial_Train
+HNTSMRG24_TumorLesionSize_Task02_Sagittal_Train
+HNTSMRG24_TumorLesionSize_Task02_Coronal_Train
+HNTSMRG24_TumorLesionSize_Task02_Axial_Train
+HNTSMRG24_TumorLesionSize_Task03_Sagittal_Train
+HNTSMRG24_TumorLesionSize_Task03_Coronal_Train
+HNTSMRG24_TumorLesionSize_Task03_Axial_Train
+HNTSMRG24_TumorLesionSize_Task04_Sagittal_Train
+HNTSMRG24_TumorLesionSize_Task04_Coronal_Train
+HNTSMRG24_TumorLesionSize_Task04_Axial_Train
+ISLES24_MaskSize_Task01_Sagittal_Train
+ISLES24_MaskSize_Task01_Coronal_Train
+ISLES24_MaskSize_Task01_Axial_Train
+ISLES24_MaskSize_Task02_Sagittal_Train
+ISLES24_MaskSize_Task02_Coronal_Train
+ISLES24_MaskSize_Task02_Axial_Train
+ISLES24_BoxSize_Task01_Sagittal_Train
+ISLES24_BoxSize_Task01_Coronal_Train
+ISLES24_BoxSize_Task01_Axial_Train
+ISLES24_BoxSize_Task02_Sagittal_Train
+ISLES24_BoxSize_Task02_Coronal_Train
+ISLES24_BoxSize_Task02_Axial_Train
+KiPA22_MaskSize_Task01_Sagittal_Train
+KiPA22_MaskSize_Task01_Coronal_Train
+KiPA22_MaskSize_Task01_Axial_Train
+KiPA22_BoxSize_Task01_Sagittal_Train
+KiPA22_BoxSize_Task01_Coronal_Train
+KiPA22_BoxSize_Task01_Axial_Train
+KiPA22_TumorLesionSize_Task01_Sagittal_Train
+KiPA22_TumorLesionSize_Task01_Coronal_Train
+KiPA22_TumorLesionSize_Task01_Axial_Train
+KiTS23_MaskSize_Task01_Sagittal_Train
+KiTS23_MaskSize_Task01_Coronal_Train
+KiTS23_MaskSize_Task01_Axial_Train
+KiTS23_BoxSize_Task01_Sagittal_Train
+KiTS23_BoxSize_Task01_Coronal_Train
+KiTS23_BoxSize_Task01_Axial_Train
+KiTS23_TumorLesionSize_Task01_Sagittal_Train
+KiTS23_TumorLesionSize_Task01_Coronal_Train
+KiTS23_TumorLesionSize_Task01_Axial_Train
+MSD_MaskSize_Task01_Sagittal_Train
+MSD_MaskSize_Task01_Coronal_Train
+MSD_MaskSize_Task01_Axial_Train
+MSD_MaskSize_Task02_Sagittal_Train
+MSD_MaskSize_Task02_Coronal_Train
+MSD_MaskSize_Task02_Axial_Train
+MSD_MaskSize_Task03_Sagittal_Train
+MSD_MaskSize_Task03_Coronal_Train
+MSD_MaskSize_Task03_Axial_Train
+MSD_MaskSize_Task04_Sagittal_Train
+MSD_MaskSize_Task04_Coronal_Train
+MSD_MaskSize_Task04_Axial_Train
+MSD_MaskSize_Task05_Sagittal_Train
+MSD_MaskSize_Task05_Coronal_Train
+MSD_MaskSize_Task05_Axial_Train
+MSD_MaskSize_Task06_Sagittal_Train
+MSD_MaskSize_Task06_Coronal_Train
+MSD_MaskSize_Task06_Axial_Train
+MSD_MaskSize_Task07_Sagittal_Train
+MSD_MaskSize_Task07_Coronal_Train
+MSD_MaskSize_Task07_Axial_Train
+MSD_MaskSize_Task08_Sagittal_Train
+MSD_MaskSize_Task08_Coronal_Train
+MSD_MaskSize_Task08_Axial_Train
+MSD_MaskSize_Task09_Sagittal_Train
+MSD_MaskSize_Task09_Coronal_Train
+MSD_MaskSize_Task09_Axial_Train
+MSD_MaskSize_Task10_Sagittal_Train
+MSD_MaskSize_Task10_Coronal_Train
+MSD_MaskSize_Task10_Axial_Train
+MSD_MaskSize_Task11_Sagittal_Train
+MSD_MaskSize_Task11_Coronal_Train
+MSD_MaskSize_Task11_Axial_Train
+MSD_MaskSize_Task12_Sagittal_Train
+MSD_MaskSize_Task12_Coronal_Train
+MSD_MaskSize_Task12_Axial_Train
+MSD_MaskSize_Task13_Sagittal_Train
+MSD_MaskSize_Task13_Coronal_Train
+MSD_MaskSize_Task13_Axial_Train
+MSD_MaskSize_Task14_Sagittal_Train
+MSD_MaskSize_Task14_Coronal_Train
+MSD_MaskSize_Task14_Axial_Train
+MSD_BoxSize_Task01_Sagittal_Train
+MSD_BoxSize_Task01_Coronal_Train
+MSD_BoxSize_Task01_Axial_Train
+MSD_BoxSize_Task02_Sagittal_Train
+MSD_BoxSize_Task02_Coronal_Train
+MSD_BoxSize_Task02_Axial_Train
+MSD_BoxSize_Task03_Sagittal_Train
+MSD_BoxSize_Task03_Coronal_Train
+MSD_BoxSize_Task03_Axial_Train
+MSD_BoxSize_Task04_Sagittal_Train
+MSD_BoxSize_Task04_Coronal_Train
+MSD_BoxSize_Task04_Axial_Train
+MSD_BoxSize_Task05_Sagittal_Train
+MSD_BoxSize_Task05_Coronal_Train
+MSD_BoxSize_Task05_Axial_Train
+MSD_BoxSize_Task06_Sagittal_Train
+MSD_BoxSize_Task06_Coronal_Train
+MSD_BoxSize_Task06_Axial_Train
+MSD_BoxSize_Task07_Sagittal_Train
+MSD_BoxSize_Task07_Coronal_Train
+MSD_BoxSize_Task07_Axial_Train
+MSD_BoxSize_Task08_Sagittal_Train
+MSD_BoxSize_Task08_Coronal_Train
+MSD_BoxSize_Task08_Axial_Train
+MSD_BoxSize_Task09_Sagittal_Train
+MSD_BoxSize_Task09_Coronal_Train
+MSD_BoxSize_Task09_Axial_Train
+MSD_BoxSize_Task10_Sagittal_Train
+MSD_BoxSize_Task10_Coronal_Train
+MSD_BoxSize_Task10_Axial_Train
+MSD_BoxSize_Task11_Sagittal_Train
+MSD_BoxSize_Task11_Coronal_Train
+MSD_BoxSize_Task11_Axial_Train
+MSD_BoxSize_Task12_Sagittal_Train
+MSD_BoxSize_Task12_Coronal_Train
+MSD_BoxSize_Task12_Axial_Train
+MSD_BoxSize_Task13_Sagittal_Train
+MSD_BoxSize_Task13_Coronal_Train
+MSD_BoxSize_Task13_Axial_Train
+MSD_BoxSize_Task14_Sagittal_Train
+MSD_BoxSize_Task14_Coronal_Train
+MSD_BoxSize_Task14_Axial_Train
+MSD_TumorLesionSize_Task01_Sagittal_Train
+MSD_TumorLesionSize_Task01_Coronal_Train
+MSD_TumorLesionSize_Task01_Axial_Train
+MSD_TumorLesionSize_Task02_Sagittal_Train
+MSD_TumorLesionSize_Task02_Coronal_Train
+MSD_TumorLesionSize_Task02_Axial_Train
+MSD_TumorLesionSize_Task03_Sagittal_Train
+MSD_TumorLesionSize_Task03_Coronal_Train
+MSD_TumorLesionSize_Task03_Axial_Train
+MSD_TumorLesionSize_Task04_Sagittal_Train
+MSD_TumorLesionSize_Task04_Coronal_Train
+MSD_TumorLesionSize_Task04_Axial_Train
+MSD_TumorLesionSize_Task05_Sagittal_Train
+MSD_TumorLesionSize_Task05_Coronal_Train
+MSD_TumorLesionSize_Task05_Axial_Train
+MSD_TumorLesionSize_Task06_Sagittal_Train
+MSD_TumorLesionSize_Task06_Coronal_Train
+MSD_TumorLesionSize_Task06_Axial_Train
+MSD_TumorLesionSize_Task07_Sagittal_Train
+MSD_TumorLesionSize_Task07_Coronal_Train
+MSD_TumorLesionSize_Task07_Axial_Train
+MSD_TumorLesionSize_Task08_Sagittal_Train
+MSD_TumorLesionSize_Task08_Coronal_Train
+MSD_TumorLesionSize_Task08_Axial_Train
+OAIZIB-CM_MaskSize_Task01_Sagittal_Train
+OAIZIB-CM_MaskSize_Task01_Coronal_Train
+OAIZIB-CM_MaskSize_Task01_Axial_Train
+OAIZIB-CM_BoxSize_Task01_Sagittal_Train
+OAIZIB-CM_BoxSize_Task01_Coronal_Train
+OAIZIB-CM_BoxSize_Task01_Axial_Train
+SKM-TEA_MaskSize_Task01_Sagittal_Train
+SKM-TEA_MaskSize_Task01_Coronal_Train
+SKM-TEA_MaskSize_Task01_Axial_Train
+SKM-TEA_MaskSize_Task02_Sagittal_Train
+SKM-TEA_MaskSize_Task02_Coronal_Train
+SKM-TEA_MaskSize_Task02_Axial_Train
+SKM-TEA_BoxSize_Task01_Sagittal_Train
+SKM-TEA_BoxSize_Task01_Coronal_Train
+SKM-TEA_BoxSize_Task01_Axial_Train
+SKM-TEA_BoxSize_Task02_Sagittal_Train
+SKM-TEA_BoxSize_Task02_Coronal_Train
+SKM-TEA_BoxSize_Task02_Axial_Train
+ToothFairy2_MaskSize_Task01_Sagittal_Train
+ToothFairy2_MaskSize_Task01_Coronal_Train
+ToothFairy2_MaskSize_Task01_Axial_Train
+ToothFairy2_BoxSize_Task01_Sagittal_Train
+ToothFairy2_BoxSize_Task01_Coronal_Train
+ToothFairy2_BoxSize_Task01_Axial_Train
+TopCoW24_MaskSize_Task01_Sagittal_Train
+TopCoW24_MaskSize_Task01_Coronal_Train
+TopCoW24_MaskSize_Task01_Axial_Train
+TopCoW24_MaskSize_Task02_Sagittal_Train
+TopCoW24_MaskSize_Task02_Coronal_Train
+TopCoW24_MaskSize_Task02_Axial_Train
+TopCoW24_BoxSize_Task01_Sagittal_Train
+TopCoW24_BoxSize_Task01_Coronal_Train
+TopCoW24_BoxSize_Task01_Axial_Train
+TopCoW24_BoxSize_Task02_Sagittal_Train
+TopCoW24_BoxSize_Task02_Coronal_Train
+TopCoW24_BoxSize_Task02_Axial_Train
+TotalSegmentator_MaskSize_Task01_Sagittal_Train
+TotalSegmentator_MaskSize_Task01_Coronal_Train
+TotalSegmentator_MaskSize_Task01_Axial_Train
+TotalSegmentator_MaskSize_Task02_Sagittal_Train
+TotalSegmentator_MaskSize_Task02_Coronal_Train
+TotalSegmentator_MaskSize_Task02_Axial_Train
+TotalSegmentator_BoxSize_Task01_Sagittal_Train
+TotalSegmentator_BoxSize_Task01_Coronal_Train
+TotalSegmentator_BoxSize_Task01_Axial_Train
+TotalSegmentator_BoxSize_Task02_Sagittal_Train
+TotalSegmentator_BoxSize_Task02_Coronal_Train
+TotalSegmentator_BoxSize_Task02_Axial_Train
+AFIDs_BiometricsFromLandmarks_Task01_Sagittal_Train
+AFIDs_BiometricsFromLandmarks_Task01_Axial_Train
+DEEP-PSMA_MaskSize_Task01_Sagittal_Train
+DEEP-PSMA_MaskSize_Task01_Coronal_Train
+DEEP-PSMA_MaskSize_Task01_Axial_Train
+DEEP-PSMA_MaskSize_Task02_Sagittal_Train
+DEEP-PSMA_MaskSize_Task02_Coronal_Train
+DEEP-PSMA_MaskSize_Task02_Axial_Train
+DEEP-PSMA_BoxSize_Task01_Sagittal_Train
+DEEP-PSMA_BoxSize_Task01_Coronal_Train
+DEEP-PSMA_BoxSize_Task01_Axial_Train
+DEEP-PSMA_BoxSize_Task02_Sagittal_Train
+DEEP-PSMA_BoxSize_Task02_Coronal_Train
+DEEP-PSMA_BoxSize_Task02_Axial_Train
+DEEP-PSMA_TumorLesionSize_Task01_Axial_Train
+DEEP-PSMA_TumorLesionSize_Task02_Axial_Train
+LIDC-IDRI_BoxSize_Task01_Sagittal_Train
+LIDC-IDRI_BoxSize_Task01_Coronal_Train
+LIDC-IDRI_BoxSize_Task01_Axial_Train
+LIDC-IDRI_MaskSize_Task01_Sagittal_Train
+LIDC-IDRI_MaskSize_Task01_Coronal_Train
+LIDC-IDRI_MaskSize_Task01_Axial_Train
+LIDC-IDRI_TumorLesionSize_Task01_Sagittal_Train
+LIDC-IDRI_TumorLesionSize_Task01_Coronal_Train
+LIDC-IDRI_TumorLesionSize_Task01_Axial_Train
+LNQ2023_BoxSize_Task01_Sagittal_Train
+LNQ2023_BoxSize_Task01_Coronal_Train
+LNQ2023_BoxSize_Task01_Axial_Train
+LNQ2023_MaskSize_Task01_Sagittal_Train
+LNQ2023_MaskSize_Task01_Coronal_Train
+LNQ2023_MaskSize_Task01_Axial_Train
+LNQ2023_TumorLesionSize_Task01_Axial_Train
+MAMA-MIA_BoxSize_Task01_Sagittal_Train
+MAMA-MIA_BoxSize_Task01_Coronal_Train
+MAMA-MIA_BoxSize_Task01_Axial_Train
+MAMA-MIA_MaskSize_Task01_Sagittal_Train
+MAMA-MIA_MaskSize_Task01_Coronal_Train
+MAMA-MIA_MaskSize_Task01_Axial_Train
+MAMA-MIA_TumorLesionSize_Task01_Sagittal_Train
+MAMA-MIA_TumorLesionSize_Task01_Coronal_Train
+MAMA-MIA_TumorLesionSize_Task01_Axial_Train
+PDDCA_MaskSize_Task01_Sagittal_Train
+PDDCA_MaskSize_Task01_Coronal_Train
+PDDCA_MaskSize_Task01_Axial_Train
+PDDCA_BoxSize_Task01_Sagittal_Train
+PDDCA_BoxSize_Task01_Coronal_Train
+PDDCA_BoxSize_Task01_Axial_Train
+PDDCA_BiometricsFromLandmarks_Task01_Sagittal_Train
+PDDCA_BiometricsFromLandmarks_Task01_Axial_Train
+PI-CAI_BoxSize_Task01_Sagittal_Train
+PI-CAI_BoxSize_Task01_Coronal_Train
+PI-CAI_BoxSize_Task01_Axial_Train
+PI-CAI_MaskSize_Task01_Sagittal_Train
+PI-CAI_MaskSize_Task01_Coronal_Train
+PI-CAI_MaskSize_Task01_Axial_Train
+PI-CAI_TumorLesionSize_Task01_Sagittal_Train
+PI-CAI_TumorLesionSize_Task01_Coronal_Train
+PI-CAI_TumorLesionSize_Task01_Axial_Train
+VerSe_MaskSize_Task01_Sagittal_Train
+VerSe_MaskSize_Task01_Coronal_Train
+VerSe_MaskSize_Task01_Axial_Train
+VerSe_BoxSize_Task01_Sagittal_Train
+VerSe_BoxSize_Task01_Coronal_Train
+VerSe_BoxSize_Task01_Axial_Train
+VerSe_BiometricsFromLandmarks_Task01_Sagittal_Train
+MSWAL_MaskSize_Task01_Sagittal_Train
+MSWAL_MaskSize_Task01_Coronal_Train
+MSWAL_MaskSize_Task01_Axial_Train
+MSWAL_BoxSize_Task01_Sagittal_Train
+MSWAL_BoxSize_Task01_Coronal_Train
+MSWAL_BoxSize_Task01_Axial_Train
+MSWAL_TumorLesionSize_Task01_Sagittal_Train
+MSWAL_TumorLesionSize_Task01_Coronal_Train
+MSWAL_TumorLesionSize_Task01_Axial_Train
+MSWAL_TumorLesionSize_Task02_Sagittal_Train
+MSWAL_TumorLesionSize_Task02_Coronal_Train
+MSWAL_TumorLesionSize_Task02_Axial_Train
+MSWAL_TumorLesionSize_Task03_Sagittal_Train
+MSWAL_TumorLesionSize_Task03_Coronal_Train
+MSWAL_TumorLesionSize_Task03_Axial_Train
+MSWAL_TumorLesionSize_Task04_Sagittal_Train
+MSWAL_TumorLesionSize_Task04_Coronal_Train
+MSWAL_TumorLesionSize_Task04_Axial_Train
+MSWAL_TumorLesionSize_Task05_Sagittal_Train
+MSWAL_TumorLesionSize_Task05_Coronal_Train
+MSWAL_TumorLesionSize_Task05_Axial_Train

scripts/_medvision_test_support.py

@@ -25,7 +25,7 @@ import types
 
 _HERE = os.path.dirname(os.path.abspath(__file__))
 MEDVISION_PY = os.path.join(_HERE, "..", "MedVision.py")
-INFO_CSV = os.path.join(_HERE, "..", "info", "v1.2.0", "ConfigurationsList_All.csv")
+INFO_CSV = os.path.join(_HERE, "..", "info", "v1.3.0", "ConfigurationsList_All.csv")
 
 
 def install_datasets_stub():

scripts/gen-annotations/dataset_specs.py

@@ -363,4 +363,16 @@ DATASETS = {
                 "mapped through native->world->RAS+ at download time. Only 250 of the 325 "
                 "scans contain all of L1-L5, so biometry uses Images-lumbar/.",
     },
+    # ------------------------------------------- added in v1.3.0: reorient in download
+    "MSWAL": {
+        "pkg": "MSWAL",
+        "download": "download_raw",
+        "supports_max_workers": True,
+        "steps": ["segmentation", "detection", "biometry"],
+        "reorient": "download",
+        "requires_env": [],
+        "optional_env": [],
+        "note": "Upstream test split (210 cases) was never uploaded to HF; the 484 "
+                "published imagesTr cases are re-split by the planner (seed 1024, 0.7).",
+    },
 }

scripts/test_annotation_resolution.py

@@ -36,8 +36,8 @@ _INFO_CSV = _support.INFO_CSV
 
 mv = _support.load_loader("medvision_res_test_")
 
-PINS = ["1.0.0", "1.1.0", "1.1.1", "1.2.0", "1.2.1", "latest"]
-RELEASE = "1.2.1"
+PINS = ["1.0.0", "1.1.0", "1.1.1", "1.2.0", "1.2.1", "1.3.0", "latest"]
+RELEASE = "1.3.0"
 
 _results = []
 
@@ -97,8 +97,8 @@ check(mv._published_versions() ==
       "_published_versions is derived from _ANNOTATION_INDEX")
 for v in mv._published_versions():
     check(_norm(v) == v, f"published version {v!r} is accepted")
-# RE-BASED: 1.3.0 used to be accepted with a warning.
-for unknown in ("1.1.5", "1.0.1", "0.0.0", "1.3.0", "2.0.0", "999.999.999"):
+# RE-BASED twice: 1.3.0 used to sit in this list until MSWAL published it.
+for unknown in ("1.1.5", "1.0.1", "0.0.0", "1.2.2", "2.0.0", "999.999.999"):
     check(_norm(unknown) == "RAISE",
           f"unpublished version {unknown!r} -> EnvironmentError",
           "would otherwise resolve silently to an older annotation, or to nothing")
@@ -138,8 +138,8 @@ check(_built == _released,
       f"only in csv: {sorted(_released - _built)[:3]}")
 check(len(configs) == len(_released), f"{len(_released)} BUILDER_CONFIGS",
       f"got {len(configs)}")
-check(len(needed) == 72, "72 (dataset, plan-kind) pairs", f"got {len(needed)}")
-check(len({d for d, _ in needed}) == 30, "30 datasets",
+check(len(needed) == 75, "75 (dataset, plan-kind) pairs", f"got {len(needed)}")
+check(len({d for d, _ in needed}) == 31, "31 datasets",
       f"got {len({d for d, _ in needed})}")
 check(not (needed - declared), "every config's pair is declared",
       f"missing: {sorted(needed - declared)}")
@@ -204,15 +204,16 @@ else:
     print(f"       reconciled {seen} pair(s)")
 
 # ------------------------------------------------------------ 6. full sweep
-section("6. Full sweep: 950 configs x every pin")
+section("6. Full sweep: 992 configs x every pin")
 
 # 1.2.0 still resolves for every config: v1.2.0 stays DECLARED in the index
 # (MAMA-MIA/PI-CAI are withheld by _PAUSED_ANNOTATIONS, not by de-listing),
 # so resolution is unchanged and only the pause gate refuses those loads.
 # 1.2.0 no longer covers the whole catalogue: MAMA-MIA and PI-CAI withdrew their
 # v1.2.0 annotations, so their 36 configs have nothing at or below that pin.
-EXPECTED = {"1.0.0": (820, 130), "1.1.0": (820, 130), "1.1.1": (820, 130),
-            "1.2.0": (914, 36), "1.2.1": (950, 0), "latest": (950, 0)}
+EXPECTED = {"1.0.0": (820, 172), "1.1.0": (820, 172), "1.1.1": (820, 172),
+            "1.2.0": (914, 78), "1.2.1": (950, 42), "1.3.0": (992, 0),
+            "latest": (992, 0)}
 
 for pin in PINS:
     requested = mv._normalize_requested(pin, RELEASE)
@@ -258,6 +259,15 @@ for ds in _INTRODUCED_120 + _WITHDREW_120:
         check(mv._resolve(declared, earliest) == earliest,
               f"{ds}/{kind} resolves at {earliest}")
 
+_INTRODUCED_130 = ["MSWAL"]
+for ds in _INTRODUCED_130:
+    for kind in mv._ANNOTATION_INDEX[ds]:
+        declared = mv._declared_versions(ds, kind)
+        check(mv._resolve(declared, "1.2.1") is None,
+              f"{ds}/{kind} unavailable at 1.2.1")
+        check(mv._resolve(declared, "1.3.0") == "1.3.0",
+              f"{ds}/{kind} resolves at 1.3.0")
+
 # ------------------------------------------------------- 7. download decision
 section("7. Download decision")
 

scripts/test_tl_ack_gate.py

@@ -71,7 +71,7 @@ for planner_version, latest_version, ack, expect_raise, desc in CASES:
 # The gate is handed the newest version published FOR THE PAIR being loaded,
 # and acknowledges against the repo release. This is what makes a purely
 # additive release non-breaking for datasets it did not touch.
-RELEASE = "1.2.1"
+RELEASE = "1.3.0"
 
 
 def _run_pair(planner_version, dataset_name, kind, ack):

src/medvision_ds/__version__.py

@@ -1 +1 @@
-__version__ = "1.2.1"
+__version__ = "1.3.0"

src/medvision_ds/datasets/MSWAL/download_fast.py

@@ -0,0 +1,120 @@
+import os
+import shutil
+import argparse
+import glob
+import zipfile
+from huggingface_hub import snapshot_download
+from medvision_ds.utils.preprocess_utils import move_folder
+
+
+# ====================================
+# Dataset Info [!]
+# ====================================
+# Dataset: MSWAL
+# Website: https://github.com/haochen-MBZUAI/MSWAL-
+# Official Release: https://huggingface.co/datasets/zhaodongwu/MSWAL
+# HF Release: https://huggingface.co/datasets/YongchengYAO/MSWAL-Lite
+# Format: nii.gz
+# NOTE: the HF mirror holds the preprocessed volumes (RAS+, uint16 masks, image/mask
+#       basenames aligned), so this path skips the 35 GB upstream fetch + conversion.
+# ====================================
+
+
+def download_and_extract(dataset_dir, dataset_name, **kwargs):
+    """
+    Download and extract the MSWAL dataset from the HuggingFace mirror.
+
+    NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name
+    the other arguments must be kwargs
+    """
+    # Download files
+    current_dir = os.getcwd()
+    os.chdir(dataset_dir)
+    tmp_dir = os.path.join(dataset_dir, "tmp")
+    os.makedirs(tmp_dir, exist_ok=True)
+    os.chdir(tmp_dir)
+    print(f"Downloading {dataset_name} dataset to {dataset_dir}...")
+
+    # ====================================
+    # Add download logic here [!]
+    # ====================================
+    # Download dataset (image + mask archives, sharded as data-part*.zip)
+    snapshot_download(
+        repo_id="YongchengYAO/MSWAL-Lite",
+        allow_patterns="*.zip",
+        repo_type="dataset",
+        revision="39fb50b67e1667d1bc514d47a4db4c6042537d75",  # commit hash on 2026-08-09
+        local_dir=".",
+        max_workers=kwargs.get("max_workers", 1),
+    )
+
+    # Extract all zip files
+    for zip_file in sorted(glob.glob("*.zip")):
+        print(f"extracting {zip_file}")
+        with zipfile.ZipFile(zip_file, "r") as zip_ref:
+            zip_ref.extractall(".")
+        os.remove(zip_file)
+        print(f"{zip_file} deleted")
+
+    # Move folder to dataset_dir
+    folders_to_move = [
+        "Images",
+        "Masks",
+    ]
+    for folder in folders_to_move:
+        move_folder(
+            os.path.join(tmp_dir, folder),
+            os.path.join(dataset_dir, folder),
+            create_dest=True,
+        )
+    # ====================================
+
+    print(f"Download and extraction completed for {dataset_name}")
+    os.chdir(dataset_dir)
+    shutil.rmtree(tmp_dir)
+    os.chdir(current_dir)
+
+
+def main(dir_datasets_data, dataset_name, **kwargs):
+    # Create dataset directory
+    dataset_dir = os.path.join(dir_datasets_data, dataset_name)
+    os.makedirs(dataset_dir, exist_ok=True)
+
+    # Change to dataset directory
+    os.chdir(dataset_dir)
+
+    # Download and extract dataset
+    download_and_extract(dataset_dir, dataset_name, **kwargs)
+
+
+if __name__ == "__main__":
+    # Set up argument parser
+    parser = argparse.ArgumentParser(description="Download and extract dataset")
+    parser.add_argument(
+        "-d",
+        "--dir_datasets_data",
+        help="Directory path where datasets will be stored",
+        required=True,
+    )
+    parser.add_argument(
+        "-n",
+        "--dataset_name",
+        help="Name of the dataset",
+        required=True,
+    )
+    parser.add_argument(
+        "--max_workers",
+        type=int,
+        default=1,
+        help="Maximum number of workers for download",
+    )
+    args = parser.parse_args()
+
+    # Extract known arguments and pass the rest as kwargs
+    kwargs = {"max_workers": args.max_workers}
+
+    main(
+        dir_datasets_data=args.dir_datasets_data,
+        dataset_name=args.dataset_name,
+        **kwargs,
+    )

src/medvision_ds/datasets/MSWAL/download_raw.py

@@ -0,0 +1,144 @@
+import os
+import glob
+import shutil
+import argparse
+from huggingface_hub import snapshot_download
+from medvision_ds.utils.preprocess_utils import move_folder
+from medvision_ds.utils.data_conversion import (
+    convert_mask_to_uint16_per_dir,
+    copy_img_header_to_mask,
+    reorient_niigz_RASplus_batch_inplace,
+)
+
+
+# ====================================
+# Dataset Info [!]
+# ====================================
+# Dataset: MSWAL
+# Website: https://github.com/haochen-MBZUAI/MSWAL-
+# Data: https://huggingface.co/datasets/zhaodongwu/MSWAL
+# Format: nii.gz
+# ====================================
+
+
+def download_and_extract(dataset_dir, dataset_name, **kwargs):
+    """
+    Download and extract the MSWAL dataset.
+
+    NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name
+    the other arguments must be kwargs
+    """
+    # Download files
+    current_dir = os.getcwd()
+    os.chdir(dataset_dir)
+    tmp_dir = os.path.join(dataset_dir, "tmp")
+    os.makedirs(tmp_dir, exist_ok=True)
+    os.chdir(tmp_dir)
+    print(f"Downloading {dataset_name} dataset to {dataset_dir}...")
+
+    # ====================================
+    # Add download logic here [!]
+    # ====================================
+    max_workers = kwargs.get("max_workers", 1)
+
+    # Download dataset from HuggingFace.
+    # Only the 484 training cases exist upstream; the 210-case test split in
+    # dataset.json points at imagesTs/ files that were never uploaded.
+    snapshot_download(
+        repo_id="zhaodongwu/MSWAL",
+        repo_type="dataset",
+        allow_patterns=["imagesTr/*", "labelsTr/*"],
+        revision="62c286b05194bfad259de063878355766a6bed9d",  # commit hash on 2026-08-07
+        local_dir=".",
+        max_workers=max_workers,
+    )
+
+    # Move files to standard locations, stripping the nnU-Net channel suffix so
+    # Images/ and Masks/ carry identical basenames
+    os.makedirs("Images", exist_ok=True)
+    os.makedirs("Masks", exist_ok=True)
+    for f in glob.glob(os.path.join("imagesTr", "*_0000.nii.gz")):
+        dst = os.path.basename(f).replace("_0000.nii.gz", ".nii.gz")
+        shutil.move(f, os.path.join("Images", dst))
+    for f in glob.glob(os.path.join("labelsTr", "*.nii.gz")):
+        shutil.move(f, os.path.join("Masks", os.path.basename(f)))
+
+    img_files_names = sorted(os.listdir("Images"))
+    mask_files_names = sorted(os.listdir("Masks"))
+    assert len(img_files_names) == 484 and img_files_names == mask_files_names, (
+        f"image/mask pairing mismatch: {len(img_files_names)} images, "
+        f"{len(mask_files_names)} masks"
+    )
+
+    # Remove leftover upstream folders so the recursive reorientation below
+    # cannot pick up files outside Images/ and Masks/
+    shutil.rmtree("imagesTr", ignore_errors=True)
+    shutil.rmtree("labelsTr", ignore_errors=True)
+
+    # Copy image headers to masks, then cast masks to uint16, then reorient.
+    # Order matters: reorientation must come AFTER copy_img_header_to_mask,
+    # which overwrites the mask affine with the image affine.
+    img_files = list(glob.glob(os.path.join("Images", "*.nii.gz")))
+    copy_img_header_to_mask(img_files, "Masks", workers_limit=max_workers)
+    convert_mask_to_uint16_per_dir("Masks", workers_limit=max_workers)
+    reorient_niigz_RASplus_batch_inplace(tmp_dir, workers_limit=max_workers)
+
+    # Move folder to dataset_dir
+    folders_to_move = [
+        "Images",
+        "Masks",
+    ]
+    for folder in folders_to_move:
+        move_folder(
+            os.path.join(tmp_dir, folder),
+            os.path.join(dataset_dir, folder),
+            create_dest=True,
+        )
+    # ====================================
+
+    print(f"Download and extraction completed for {dataset_name}")
+    os.chdir(dataset_dir)
+    shutil.rmtree(tmp_dir)
+    os.chdir(current_dir)
+
+
+def main(dir_datasets_data, dataset_name, **kwargs):
+    # Create dataset directory
+    dataset_dir = os.path.join(dir_datasets_data, dataset_name)
+    os.makedirs(dataset_dir, exist_ok=True)
+
+    # Change to dataset directory
+    os.chdir(dataset_dir)
+
+    # Download and extract dataset
+    download_and_extract(dataset_dir, dataset_name, **kwargs)
+
+
+if __name__ == "__main__":
+    # Set up argument parser
+    parser = argparse.ArgumentParser(description="Download and extract dataset")
+    parser.add_argument(
+        "-d",
+        "--dir_datasets_data",
+        help="Directory path where datasets will be stored",
+        required=True,
+    )
+    parser.add_argument(
+        "-n",
+        "--dataset_name",
+        help="Name of the dataset",
+        required=True,
+    )
+    parser.add_argument(
+        "--max_workers",
+        type=int,
+        default=1,
+        help="Number of parallel workers for download and conversion",
+    )
+    args = parser.parse_args()
+
+    main(
+        dir_datasets_data=args.dir_datasets_data,
+        dataset_name=args.dataset_name,
+        max_workers=args.max_workers,
+    )

src/medvision_ds/datasets/MSWAL/preprocess_biometry.py

@@ -0,0 +1,320 @@
+import os
+import argparse
+from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
+from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry_fromSeg
+
+
+# ====================================
+# Dataset Info [!]
+# ====================================
+# Dataset: MSWAL
+# Website: https://github.com/haochen-MBZUAI/MSWAL-
+# Data: https://huggingface.co/datasets/zhaodongwu/MSWAL
+# Format: nii.gz
+# ====================================
+CLUSTER_SIZE_THRESHOLD = 20
+
+dataset_info = {
+    "dataset": "MSWAL",
+    "dataset_website": "https://github.com/haochen-MBZUAI/MSWAL-",
+    "dataset_data": [
+        "https://huggingface.co/datasets/zhaodongwu/MSWAL",
+    ],
+    "license": ["CC BY-NC 4.0"],
+    "paper": [
+        "https://arxiv.org/abs/2503.13560",
+    ],
+}
+
+labels_map = {
+    "1": "gallstone",
+    "2": "kidney stone",
+    "3": "liver tumor",
+    "4": "kidney tumor",
+    "5": "pancreatic cancer",
+    "6": "liver cyst",
+    "7": "kidney cyst",
+}
+# ====================================
+
+
+# ===============
+# DO NOT CHANGE
+# ===============
+landmarks_map = {
+    "P1": "most right/anterior/superior endpoint of the major axis",
+    "P2": "most left/superior/inferior endpoint of the major axis",
+    "P3": "most right/anterior/superior endpoint of the minor axis",
+    "P4": "most left/superior/inferior endpoint of the minor axis",
+}
+
+lines_map = {
+    "L-1-2": {
+        "name": "marjor axis of the fitted ellipse",
+        "element_keys": ["P1", "P2"],
+        "element_map_name": "landmarks_map",
+    },
+    "L-3-4": {
+        "name": "minor axis of the fitted ellipse",
+        "element_keys": ["P3", "P4"],
+        "element_map_name": "landmarks_map",
+    },
+}
+
+angles_map = {}
+
+biometrics_map = [
+    {
+        "metric_type": "distance",
+        "metric_map_name": "lines_map",
+        "metric_key": "L-1-2",
+    },
+    {
+        "metric_type": "distance",
+        "metric_map_name": "lines_map",
+        "metric_key": "L-3-4",
+    },
+]
+# ===============
+
+
+# NOTE: Biometry targets are the tumor, cancer, and cyst labels (3-7) only.
+# The stone labels (1: gallstone, 2: kidney stone) are excluded by omission:
+# they are tiny, routinely multi-instance findings.
+benchmark_plan = {
+    "dataset_info": dataset_info,
+    "tasks": [
+        {
+            "image_modality": "CT",
+            "image_description": "abdominal computed tomography (CT) scan",
+            "image_folder": "Images",
+            "mask_folder": "Masks",
+            "landmark_folder": "Landmarks-Label3",
+            "landmark_figure_folder": "Landmarks-Label3-fig",
+            "image_prefix": "",
+            "image_suffix": ".nii.gz",
+            "mask_prefix": "",
+            "mask_suffix": ".nii.gz",
+            "landmark_prefix": "",
+            "landmark_suffix": ".json.gz",
+            "labels_map": labels_map,
+            "landmarks_map": landmarks_map,
+            "lines_map": lines_map,
+            "angles_map": angles_map,
+            "biometrics_map": biometrics_map,
+            "target_label": 3,
+            "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD,
+        },
+        {
+            "image_modality": "CT",
+            "image_description": "abdominal computed tomography (CT) scan",
+            "image_folder": "Images",
+            "mask_folder": "Masks",
+            "landmark_folder": "Landmarks-Label4",
+            "landmark_figure_folder": "Landmarks-Label4-fig",
+            "image_prefix": "",
+            "image_suffix": ".nii.gz",
+            "mask_prefix": "",
+            "mask_suffix": ".nii.gz",
+            "landmark_prefix": "",
+            "landmark_suffix": ".json.gz",
+            "labels_map": labels_map,
+            "landmarks_map": landmarks_map,
+            "lines_map": lines_map,
+            "angles_map": angles_map,
+            "biometrics_map": biometrics_map,
+            "target_label": 4,
+            "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD,
+        },
+        {
+            "image_modality": "CT",
+            "image_description": "abdominal computed tomography (CT) scan",
+            "image_folder": "Images",
+            "mask_folder": "Masks",
+            "landmark_folder": "Landmarks-Label5",
+            "landmark_figure_folder": "Landmarks-Label5-fig",
+            "image_prefix": "",
+            "image_suffix": ".nii.gz",
+            "mask_prefix": "",
+            "mask_suffix": ".nii.gz",
+            "landmark_prefix": "",
+            "landmark_suffix": ".json.gz",
+            "labels_map": labels_map,
+            "landmarks_map": landmarks_map,
+            "lines_map": lines_map,
+            "angles_map": angles_map,
+            "biometrics_map": biometrics_map,
+            "target_label": 5,
+            "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD,
+        },
+        {
+            "image_modality": "CT",
+            "image_description": "abdominal computed tomography (CT) scan",
+            "image_folder": "Images",
+            "mask_folder": "Masks",
+            "landmark_folder": "Landmarks-Label6",
+            "landmark_figure_folder": "Landmarks-Label6-fig",
+            "image_prefix": "",
+            "image_suffix": ".nii.gz",
+            "mask_prefix": "",
+            "mask_suffix": ".nii.gz",
+            "landmark_prefix": "",
+            "landmark_suffix": ".json.gz",
+            "labels_map": labels_map,
+            "landmarks_map": landmarks_map,
+            "lines_map": lines_map,
+            "angles_map": angles_map,
+            "biometrics_map": biometrics_map,
+            "target_label": 6,
+            "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD,
+        },
+        {
+            "image_modality": "CT",
+            "image_description": "abdominal computed tomography (CT) scan",
+            "image_folder": "Images",
+            "mask_folder": "Masks",
+            "landmark_folder": "Landmarks-Label7",
+            "landmark_figure_folder": "Landmarks-Label7-fig",
+            "image_prefix": "",
+            "image_suffix": ".nii.gz",
+            "mask_prefix": "",
+            "mask_suffix": ".nii.gz",
+            "landmark_prefix": "",
+            "landmark_suffix": ".json.gz",
+            "labels_map": labels_map,
+            "landmarks_map": landmarks_map,
+            "lines_map": lines_map,
+            "angles_map": angles_map,
+            "biometrics_map": biometrics_map,
+            "target_label": 7,
+            "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD,
+        },
+    ],
+}
+# ====================================
+
+
+def main(
+    dir_datasets_data,
+    dataset_name,
+    benchmark_plan=benchmark_plan,
+    random_seed=1024,
+    split_ratio=0.7,
+    shrunken_bbox_scale=0.9,
+    enlarged_bbox_scale=1.1,
+    force_uint16_mask=False,
+    reorient2RAS=False,
+    visualization=True,
+    annotation_version=None,
+):
+    # Create dataset directory
+    dataset_dir = os.path.join(dir_datasets_data, dataset_name)
+    os.makedirs(dataset_dir, exist_ok=True)
+
+    # Change to dataset directory
+    os.chdir(dataset_dir)
+
+    # Process dataset for segmentation task
+    planner = MedVision_BenchmarkPlannerBiometry_fromSeg(
+        dataset_dir=dataset_dir,
+        bm_plan=benchmark_plan,
+        dataset_name=dataset_name,
+        seed=random_seed,
+        split_ratio=split_ratio,
+        shrunk_bbox_scale=shrunken_bbox_scale,
+        enlarged_bbox_scale=enlarged_bbox_scale,
+        force_uint16_mask=force_uint16_mask,
+        reorient2RAS=reorient2RAS,
+        visualization=visualization,
+        num_proc=_get_cgroup_limited_cpus(),
+        version=annotation_version,
+    )
+    planner.process()
+
+
+if __name__ == "__main__":
+    # Set up argument parser
+    parser = argparse.ArgumentParser(
+        description="Generate benchmark planner for biometric measurement task."
+    )
+    parser.add_argument(
+        "-d",
+        "--dir_datasets_data",
+        type=str,
+        help="Directory path where datasets will be stored",
+        required=True,
+    )
+    parser.add_argument(
+        "-n",
+        "--dataset_name",
+        type=str,
+        help="Name of the dataset",
+        required=True,
+    )
+    parser.add_argument(
+        "--random_seed",
+        type=int,
+        default=1024,
+        help="Random seed for reproducibility",
+    )
+    parser.add_argument(
+        "--split_ratio",
+        type=float,
+        default=0.7,
+        help="Train/test split ratio (0-1)",
+    )
+    parser.add_argument(
+        "--shrunken_bbox_scale",
+        type=float,
+        default=0.9,
+        help="Scale factor for shrunken bounding box",
+    )
+    parser.add_argument(
+        "--enlarged_bbox_scale",
+        type=float,
+        default=1.1,
+        help="Scale factor for enlarged bounding box",
+    )
+    parser.add_argument(
+        "--force_uint16_mask",
+        action="store_true",
+        help="Force mask to be uint16",
+    )
+    parser.add_argument(
+        "--reorient2RAS",
+        action="store_true",
+        help="Reorient images and masks to RAS orientation",
+    )
+    parser.add_argument(
+        "--visualization",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="Save T/L ellipse landmark figures (Landmarks-Label<N>-fig); default: on",
+    )
+    parser.add_argument(
+        "--annotation_version",
+        type=str,
+        default=None,
+        help="Version stamped into benchmark_plan_*_v<X>.json.gz and the "
+        "versioned Landmarks folders; defaults to the installed medvision_ds "
+        "version. Only the CURRENT LATEST version of an annotation can be "
+        "reproduced from this codebase - the generation code changes between "
+        "versions, so naming an older one emits a file with that name but "
+        "with today's values.",
+    )
+
+    args = parser.parse_args()
+
+    main(
+        benchmark_plan=benchmark_plan,  # global variable
+        dir_datasets_data=args.dir_datasets_data,
+        dataset_name=args.dataset_name,
+        random_seed=args.random_seed,
+        split_ratio=args.split_ratio,
+        shrunken_bbox_scale=args.shrunken_bbox_scale,
+        enlarged_bbox_scale=args.enlarged_bbox_scale,
+        force_uint16_mask=args.force_uint16_mask,
+        reorient2RAS=args.reorient2RAS,
+        visualization=args.visualization,
+        annotation_version=args.annotation_version,
+    )

src/medvision_ds/datasets/MSWAL/preprocess_detection.py

@@ -0,0 +1,153 @@
+import os
+import argparse
+from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
+from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection
+
+
+# ====================================
+# Dataset Info [!]
+# ====================================
+# Dataset: MSWAL
+# Website: https://github.com/haochen-MBZUAI/MSWAL-
+# Data: https://huggingface.co/datasets/zhaodongwu/MSWAL
+# Format: nii.gz
+# ====================================
+dataset_info = {
+    "dataset": "MSWAL",
+    "dataset_website": "https://github.com/haochen-MBZUAI/MSWAL-",
+    "dataset_data": [
+        "https://huggingface.co/datasets/zhaodongwu/MSWAL",
+    ],
+    "license": ["CC BY-NC 4.0"],
+    "paper": [
+        "https://arxiv.org/abs/2503.13560",
+    ],
+}
+
+labels_map = {
+    "1": "gallstone",
+    "2": "kidney stone",
+    "3": "liver tumor",
+    "4": "kidney tumor",
+    "5": "pancreatic cancer",
+    "6": "liver cyst",
+    "7": "kidney cyst",
+}
+
+benchmark_plan = {
+    "dataset_info": dataset_info,
+    "tasks": [
+        {
+            "image_modality": "CT",
+            "image_description": "abdominal computed tomography (CT) scan",
+            "image_folder": "Images",
+            "mask_folder": "Masks",
+            "image_prefix": "",
+            "image_suffix": ".nii.gz",
+            "mask_prefix": "",
+            "mask_suffix": ".nii.gz",
+            "labels_map": labels_map,
+        },
+    ],
+}
+# ====================================
+
+
+def main(
+    dir_datasets_data,
+    dataset_name,
+    benchmark_plan=benchmark_plan,
+    random_seed=1024,
+    split_ratio=0.7,
+    force_uint16_mask=False,
+    reorient2RAS=False,
+    annotation_version=None,
+):
+    # Create dataset directory
+    dataset_dir = os.path.join(dir_datasets_data, dataset_name)
+    os.makedirs(dataset_dir, exist_ok=True)
+
+    # Change to dataset directory
+    os.chdir(dataset_dir)
+
+    # Process dataset for detection task
+    planner = MedVision_BenchmarkPlannerDetection(
+        dataset_dir=dataset_dir,
+        bm_plan=benchmark_plan,
+        dataset_name=dataset_name,
+        seed=random_seed,
+        split_ratio=split_ratio,
+        force_uint16_mask=force_uint16_mask,
+        reorient2RAS=reorient2RAS,
+        num_proc=_get_cgroup_limited_cpus(),
+        version=annotation_version,
+    )
+    planner.process()
+
+
+if __name__ == "__main__":
+    # Set up argument parser
+    parser = argparse.ArgumentParser(
+        description="Generate benchmark planner for detection task."
+    )
+    parser.add_argument(
+        "-d",
+        "--dir_datasets_data",
+        type=str,
+        help="Directory path where datasets will be stored",
+        required=True,
+    )
+    parser.add_argument(
+        "-n",
+        "--dataset_name",
+        type=str,
+        help="Name of the dataset",
+        required=True,
+    )
+    parser.add_argument(
+        "--random_seed",
+        type=int,
+        default=1024,
+        help="Random seed for reproducibility",
+    )
+    parser.add_argument(
+        "--split_ratio",
+        type=float,
+        default=0.7,
+        help="Train/test split ratio (0-1)",
+    )
+    parser.add_argument(
+        "--force_uint16_mask",
+        action="store_true",
+        help="Force mask to be uint16",
+    )
+    parser.add_argument(
+        "--reorient2RAS",
+        action="store_true",
+        help="Reorient images and masks to RAS orientation",
+    )
+
+    parser.add_argument(
+        "--annotation_version",
+        type=str,
+        default=None,
+        help="Version stamped into benchmark_plan_*_v<X>.json.gz and the "
+        "versioned Landmarks folders; defaults to the installed medvision_ds "
+        "version. Only the CURRENT LATEST version of an annotation can be "
+        "reproduced from this codebase - the generation code changes between "
+        "versions, so naming an older one emits a file with that name but "
+        "with today's values.",
+    )
+
+    args = parser.parse_args()
+
+    main(
+        benchmark_plan=benchmark_plan,  # global variable
+        dir_datasets_data=args.dir_datasets_data,
+        dataset_name=args.dataset_name,
+        random_seed=args.random_seed,
+        split_ratio=args.split_ratio,
+        force_uint16_mask=args.force_uint16_mask,
+        reorient2RAS=args.reorient2RAS,
+        annotation_version=args.annotation_version,
+    )

src/medvision_ds/datasets/MSWAL/preprocess_segmentation.py

@@ -0,0 +1,153 @@
+import os
+import argparse
+from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
+from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation
+
+
+# ====================================
+# Dataset Info [!]
+# ====================================
+# Dataset: MSWAL
+# Website: https://github.com/haochen-MBZUAI/MSWAL-
+# Data: https://huggingface.co/datasets/zhaodongwu/MSWAL
+# Format: nii.gz
+# ====================================
+dataset_info = {
+    "dataset": "MSWAL",
+    "dataset_website": "https://github.com/haochen-MBZUAI/MSWAL-",
+    "dataset_data": [
+        "https://huggingface.co/datasets/zhaodongwu/MSWAL",
+    ],
+    "license": ["CC BY-NC 4.0"],
+    "paper": [
+        "https://arxiv.org/abs/2503.13560",
+    ],
+}
+
+labels_map = {
+    "1": "gallstone",
+    "2": "kidney stone",
+    "3": "liver tumor",
+    "4": "kidney tumor",
+    "5": "pancreatic cancer",
+    "6": "liver cyst",
+    "7": "kidney cyst",
+}
+
+benchmark_plan = {
+    "dataset_info": dataset_info,
+    "tasks": [
+        {
+            "image_modality": "CT",
+            "image_description": "abdominal computed tomography (CT) scan",
+            "image_folder": "Images",
+            "mask_folder": "Masks",
+            "image_prefix": "",
+            "image_suffix": ".nii.gz",
+            "mask_prefix": "",
+            "mask_suffix": ".nii.gz",
+            "labels_map": labels_map,
+        },
+    ],
+}
+# ====================================
+
+
+def main(
+    dir_datasets_data,
+    dataset_name,
+    benchmark_plan=benchmark_plan,
+    random_seed=1024,
+    split_ratio=0.7,
+    force_uint16_mask=False,
+    reorient2RAS=False,
+    annotation_version=None,
+):
+    # Create dataset directory
+    dataset_dir = os.path.join(dir_datasets_data, dataset_name)
+    os.makedirs(dataset_dir, exist_ok=True)
+
+    # Change to dataset directory
+    os.chdir(dataset_dir)
+
+    # Process dataset for segmentation task
+    planner = MedVision_BenchmarkPlannerSegmentation(
+        dataset_dir=dataset_dir,
+        bm_plan=benchmark_plan,
+        dataset_name=dataset_name,
+        seed=random_seed,
+        split_ratio=split_ratio,
+        force_uint16_mask=force_uint16_mask,
+        reorient2RAS=reorient2RAS,
+        num_proc=_get_cgroup_limited_cpus(),
+        version=annotation_version,
+    )
+    planner.process()
+
+
+if __name__ == "__main__":
+    # Set up argument parser
+    parser = argparse.ArgumentParser(
+        description="Generate benchmark planner for segmentation task."
+    )
+    parser.add_argument(
+        "-d",
+        "--dir_datasets_data",
+        type=str,
+        help="Directory path where datasets will be stored",
+        required=True,
+    )
+    parser.add_argument(
+        "-n",
+        "--dataset_name",
+        type=str,
+        help="Name of the dataset",
+        required=True,
+    )
+    parser.add_argument(
+        "--random_seed",
+        type=int,
+        default=1024,
+        help="Random seed for reproducibility",
+    )
+    parser.add_argument(
+        "--split_ratio",
+        type=float,
+        default=0.7,
+        help="Train/test split ratio (0-1)",
+    )
+    parser.add_argument(
+        "--force_uint16_mask",
+        action="store_true",
+        help="Force mask to be uint16",
+    )
+    parser.add_argument(
+        "--reorient2RAS",
+        action="store_true",
+        help="Reorient images and masks to RAS orientation",
+    )
+
+    parser.add_argument(
+        "--annotation_version",
+        type=str,
+        default=None,
+        help="Version stamped into benchmark_plan_*_v<X>.json.gz and the "
+        "versioned Landmarks folders; defaults to the installed medvision_ds "
+        "version. Only the CURRENT LATEST version of an annotation can be "
+        "reproduced from this codebase - the generation code changes between "
+        "versions, so naming an older one emits a file with that name but "
+        "with today's values.",
+    )
+
+    args = parser.parse_args()
+
+    main(
+        benchmark_plan=benchmark_plan,  # global variable
+        dir_datasets_data=args.dir_datasets_data,
+        dataset_name=args.dataset_name,
+        random_seed=args.random_seed,
+        split_ratio=args.split_ratio,
+        force_uint16_mask=args.force_uint16_mask,
+        reorient2RAS=args.reorient2RAS,
+        annotation_version=args.annotation_version,
+    )

src/medvision_ds/datasets/__init__.py

@@ -30,6 +30,7 @@ from . import (
     PDDCA,
     PICAI,
     VerSe,
+    MSWAL,
 )
 
 __all__ = [
@@ -62,5 +63,6 @@ __all__ = [
     "MAMA_MIA",
     "PDDCA",
     "PICAI",
-    "VerSe"
+    "VerSe",
+    "MSWAL"
 ]

src/medvision_ds/utils/image_normalization.py

@@ -311,6 +311,11 @@ LABEL_MAP_REGROUP = {
     "right submandibular gland": "Head-Neck",
     "vertebra L6": "Spine",
     "vertebra T13": "Spine",
+    # --- v1.3.0: labels introduced by MSWAL ---
+    "gallstone": "Gallbladder",
+    "kidney stone": "Kidney Tumor/Lesion",
+    "liver cyst": "Liver Tumor/Lesion",
+    "pancreatic cancer": "Pancreas Tumor/Lesion",
 }
 
 

src/pyproject.toml

@@ -85,6 +85,7 @@ packages = [
     "medvision_ds.datasets.PDDCA",
     "medvision_ds.datasets.PICAI",
     "medvision_ds.datasets.VerSe",
+    "medvision_ds.datasets.MSWAL",
 ]
 package-dir = { "" = "." }