Datasets:

ChipYTY
/

PASD

@@ -1,147 +1,127 @@
----
-license: mit
-task_categories:
-  - image-segmentation
-  - image-classification
-language:
-  - en
-tags:
-  - medical-imaging
-  - mri
-  - 3d
-  - placenta
-  - pas
-  - placenta-accreta-spectrum
-  - obstetrics
-size_categories:
-  - n<1K
-pretty_name: PASD - Placenta Accreta Spectrum MRI Dataset
----
-# PASD — Placenta Accreta Spectrum MRI Dataset
-A 3D MRI dataset for **Placenta Accreta Spectrum (PAS)** diagnosis with
-voxel-level lesion masks and case-level diagnostic labels. This dataset
-accompanies the paper:
-> **3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum**, IEEE Transactions on Image Processing.
-Source code for the proposed 3DSAMba method:
-[https://github.com/Drchip61/PASD](https://github.com/Drchip61/PASD).
-## Dataset Summary
-| Split | Cases | Negative (label=0) | Positive (label=1) |
-| ----- | ----- | ------------------ | ------------------ |
-| train | 184   | 61                 | 123                |
-| test  | 60    | 20                 | 40                 |
-| total | 244   | 81                 | 163                |
-Each case contains a single transverse-plane T2-weighted MRI volume of the
-uterus and the corresponding binary segmentation mask covering the suspected
-lesion region. Volumes are saved as NIfTI files (`.nii.gz`) at their native
-resolution; typical shape is `(560, 560, ~55-70)` with `float64` intensities
-in roughly `[0, 3500]`.
-## Files & Layout
-```
-PASD/
-├── train/
-│   ├── PASD_00001_1/
-│   │   ├── PASD_00001_1_image.nii.gz   # MRI volume
-│   │   └── mask.nii.gz                 # binary segmentation mask
-│   ├── PASD_00002_1/
-│   │   └── ...
-│   └── PASD_00184_1/
-└── test/
-    ├── PASD_00185_1/
-    │   └── ...
-    └── PASD_00244_0/
-```
-- The directory name encodes the case id and the **case-level class label**
-  (`PASD_<5-digit-id>_<label>`), where `label ∈ {0, 1}` indicates
-  PAS-negative or PAS-positive respectively.
-- Inside every case directory there is exactly one MRI volume
-  (`*_image.nii.gz`) and one segmentation mask (`mask.nii.gz`).
-This layout is the one expected by the dataloaders in the reference
-implementation. The classifier-stage `dataset_class.py` additionally reads
-predicted masks from a sibling directory (`test_other/`) — see the
-repository for details.
-## Privacy / De-identification
-All cases have been **fully de-identified**:
-- Original patient-name pinyin and hospital sequence numbers have been
-  removed from both directory names and file names.
-- NIfTI header fields that *could* contain free text (`descrip`,
-  `intent_name`, `aux_file`, `db_name`) are emptied. They were already empty
-  in the source data, but we scrub them defensively.
-- No DICOM tags, accession numbers, or acquisition timestamps are
-  distributed with the dataset.
-The internal mapping between original case identifiers and the released
-`PASD_xxxxx` ids is **not** part of this release and is kept only by the
-data custodians.
-## How to Load
-### Plain PyTorch
-```python
-import os
-import nibabel as nib
-CASE_DIR = "PASD/train/PASD_00001_1"
-mri = nib.load(os.path.join(CASE_DIR, "PASD_00001_1_image.nii.gz")).get_fdata()
-msk = nib.load(os.path.join(CASE_DIR, "mask.nii.gz")).get_fdata()
-label = int(CASE_DIR[-1])    # 0 = PAS-negative, 1 = PAS-positive
-print(mri.shape, msk.shape, label)
-```
-### Hugging Face Datasets
-```python
-from huggingface_hub import snapshot_download
-local_dir = snapshot_download(
-    repo_id="ChipYTY/PASD",
-    repo_type="dataset",
-)
-```
-After the snapshot is available locally, the `train/` and `test/` folders
-can be plugged directly into the reference implementation's `dataset.py`.
-## Intended Use
-- Lesion segmentation on placenta-region MRI.
-- PAS positive vs. negative classification.
-- Multi-task learning that couples segmentation and diagnosis.
-The dataset is intended for research purposes only. It is **not** a
-substitute for clinical judgement and should not be used to make individual
-diagnoses.
-## Citation
-```bibtex
-@article{zhang2025pasd,
-  title   = {3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum},
-  author  = {Zhang, Yuliang and He, Fang and Peng, Lulu and Guo, Qing and Yu, Lin and
-             Wang, Zhijian and Shun, Wei and Liu, Jue and Chen, Yonglu and Huang, Jianwei and
-             Bao, Zeye and Cai, Zhishan and Chen, Yanhong and Hu, Miao and Gu, Zhongjia and
-             Shi, Yiyu and Yan, Tianyu and Zhang, Pingping and Ting, Song and Du, Lili and Chen, Dunjin},
-  journal = {IEEE Transactions on Image Processing},
-  year    = {2025}
-}
-```
-## License
-Released under the [MIT License](https://opensource.org/license/mit/).

+---
+language:
+- en
+license: mit
+size_categories:
+- n<1K
+task_categories:
+- image-segmentation
+- image-classification
+pretty_name: PASD - Placenta Accreta Spectrum MRI Dataset
+tags:
+- medical-imaging
+- mri
+- 3d
+- placenta
+- pas
+- placenta-accreta-spectrum
+- obstetrics
+---
+# PASD — Placenta Accreta Spectrum MRI Dataset
+A 3D MRI dataset for **Placenta Accreta Spectrum (PAS)** diagnosis with voxel-level lesion masks and case-level diagnostic labels. This dataset accompanies the paper:
+> **[3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum](https://huggingface.co/papers/2606.00489)**, IEEE Transactions on Image Processing.
+Source code for the proposed 3DSAMba method: [https://github.com/Drchip61/PASD](https://github.com/Drchip61/PASD).
+## Dataset Summary
+| Split | Cases | Negative (label=0) | Positive (label=1) |
+| ----- | ----- | ------------------ | ------------------ |
+| train | 184   | 61                 | 123                |
+| test  | 60    | 20                 | 40                 |
+| total | 244   | 81                 | 163                |
+Each case contains a single transverse-plane T2-weighted MRI volume of the uterus and the corresponding binary segmentation mask covering the suspected lesion region. Volumes are saved as NIfTI files (`.nii.gz`) at their native resolution; typical shape is `(560, 560, ~55-70)` with `float64` intensities in roughly `[0, 3500]`.
+## Files & Layout
+```
+PASD/
+├── train/
+│   ├── PASD_00001_1/
+│   │   ├── PASD_00001_1_image.nii.gz   # MRI volume
+│   │   └── mask.nii.gz                 # binary segmentation mask
+│   ├── PASD_00002_1/
+│   │   └── ...
+│   └── PASD_00184_1/
+└── test/
+    ├── PASD_00185_1/
+    │   └── ...
+    └── PASD_00244_0/
+```
+- The directory name encodes the case id and the **case-level class label** (`PASD_<5-digit-id>_<label>`), where `label ∈ {0, 1}` indicates PAS-negative or PAS-positive respectively.
+- Inside every case directory there is exactly one MRI volume (`*_image.nii.gz`) and one segmentation mask (`mask.nii.gz`).
+This layout is the one expected by the dataloaders in the reference implementation. The classifier-stage `dataset_class.py` additionally reads predicted masks from a sibling directory (`test_other/`) — see the repository for details.
+## Privacy / De-identification
+All cases have been **fully de-identified**:
+- Original patient-name pinyin and hospital sequence numbers have been removed from both directory names and file names.
+- NIfTI header fields that *could* contain free text (`descrip`, `intent_name`, `aux_file`, `db_name`) are emptied. They were already empty in the source data, but we scrub them defensively.
+- No DICOM tags, accession numbers, or acquisition timestamps are distributed with the dataset.
+The internal mapping between original case identifiers and the released `PASD_xxxxx` ids is **not** part of this release and is kept only by the data custodians.
+## How to Load
+### Downloading via Hugging Face Hub
+You can download the dataset using the `huggingface_hub` library:
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id="ChipYTY/PASD",
+    repo_type="dataset",
+    local_dir=".",
+    allow_patterns=["train/**", "test/**"],
+)
+```
+### Loading MRI Data
+```python
+import os
+import nibabel as nib
+CASE_DIR = "PASD/train/PASD_00001_1"
+mri = nib.load(os.path.join(CASE_DIR, "PASD_00001_1_image.nii.gz")).get_fdata()
+msk = nib.load(os.path.join(CASE_DIR, "mask.nii.gz")).get_fdata()
+label = int(CASE_DIR[-1])    # 0 = PAS-negative, 1 = PAS-positive
+print(mri.shape, msk.shape, label)
+```
+## Intended Use
+- Lesion segmentation on placenta-region MRI.
+- PAS positive vs. negative classification.
+- Multi-task learning that couples segmentation and diagnosis.
+The dataset is intended for research purposes only. It is **not** a substitute for clinical judgement and should not be used to make individual diagnoses.
+## Citation
+```bibtex
+@article{zhang2025pasd,
+  title   = {3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum},
+  author  = {Zhang, Yuliang and He, Fang and Peng, Lulu and Guo, Qing and Yu, Lin and
+             Wang, Zhijian and Shun, Wei and Liu, Jue and Chen, Yonglu and Huang, Jianwei and
+             Bao, Zeye and Cai, Zhishan and Chen, Yanhong and Hu, Miao and Gu, Zhongjia and
+             Shi, Yiyu and Yan, Tianyu and Zhang, Pingping and Ting, Song and Du, Lili and Chen, Dunjin},
+  journal = {IEEE Transactions on Image Processing},
+  year    = {2025}
+}
+```
+## License
+Released under the [MIT License](https://opensource.org/license/mit/).