Initial datacard

README.md

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+---
+license: other
+language:
+- en
+pretty_name: MedSeg-7D
+size_categories:
+- 10K<n<100K
+task_categories:
+- image-segmentation
+tags:
+- medical-imaging
+- segmentation
+- diffusion-augmentation
+- endoscopy
+- dermoscopy
+- ultrasound
+- fundus
+- mri
+---
+
+# MedSeg-7D: Seven Public 2D Medical Segmentation Benchmarks (with Canonical Splits)
+
+A curated bundle of seven public 2D medical segmentation datasets, packaged
+with **canonical leakage-free splits** for the four datasets where one is
+needed (ACDC patient-level, BraTS2020 volume-level, CVC-ClinicDB
+video-level, plus seed-fixed image-level for the rest). All raw images and
+masks are retained at their original resolution; no resizing, no
+preprocessing baked in.
+
+This is the **dataset-only release** that accompanied an evaluation-protocol
+audit of pixel- vs.\ latent-space diffusion augmentation for medical image
+segmentation. The bundle is reusable for any 2D medical-segmentation
+research, not just the original study.
+
+> **Why this exists.** Many existing medical-augmentation papers report
+> non-comparable numbers because each uses a different (often undocumented)
+> train/test split, and several datasets have hidden leakage if split at
+> the image level (CVC same-video frames, ACDC same-patient slices, BraTS
+> same-volume slices). This release fixes one canonical split per dataset
+> so future work can be paired-comparable.
+
+---
+
+## Contents
+
+```
+MedSeg-7D/
+├── README.md
+├── ACDC/                         (cardiac MRI, 100 patients → 1841 slices)
+│   ├── images/                   patient<id>_frame<f>_slice_<s>.png
+│   ├── masks/                    matching mask filenames
+│   └── split_info.json           CANONICAL patient-level split (seed=42, 80/20)
+│
+├── BraTS2020/                    (brain MRI FLAIR, 369 volumes → 22677 slices)
+│   ├── images/                   volume_<id>_slice_<s>.png  (FLAIR channel)
+│   ├── masks/                    matching whole-tumor binary mask
+│   └── split_info.json           CANONICAL volume-level split (seed=42, 295/74)
+│
+├── BUSI/                         (breast ultrasound, 780 images)
+│   ├── images/
+│   └── masks/                    masks suffixed _mask.png
+│
+├── CVC-ClinicDB/                 (endoscopy polyp, 612 frames / 29 video sequences)
+│   ├── PNG/
+│   │   ├── Original/             RGB frames
+│   │   └── Ground Truth/         binary masks
+│   ├── TIF/                      original release format
+│   ├── metadata.csv              per-frame metadata
+│   ├── class_dict.csv
+│   └── video_split_seed42.json   CANONICAL video-level split (23 train / 6 test sequences)
+│
+├── Kvasir-SEG/                   (endoscopy polyp, 1000 images)
+│   ├── images/                   RGB frames
+│   ├── masks/                    binary masks
+│   ├── bbox/                     bounding boxes (auxiliary)
+│   └── kavsir_seg_README.md      original release notes
+│
+├── REFUGE2/                      (fundus optic disc, 400 images)
+│   ├── train/                    {images/, mask/}
+│   ├── val/                      {images/, mask/}
+│   └── test/                     {images/, mask/}
+│
+└── ISIC2018/                     (dermoscopy lesions, 2594 train + 100 val + 1000 test)
+    ├── train/                    {images/, masks/}
+    ├── validation/               {images/, masks/}
+    └── test/                     {images/, masks/}
+```
+
+Approximate total size: ~18 GB.
+
+---
+
+## Per-dataset details
+
+### 1. ACDC — Cardiac cine-MRI
+
+| | |
+|---|---|
+| **Modality** | Cardiac cine-MRI (2D slices) |
+| **Original task** | Multi-class cardiac structure segmentation |
+| **Patients / slices** | 100 / 1841 |
+| **Mask classes** | 4 (background, RV, myocardium, LV) — preserved as in the original release |
+| **Split type (canonical)** | **Patient-level**, 80 train / 20 test, seed=42 |
+| **Split file** | `ACDC/split_info.json` |
+| **Leakage risk** | None at patient level. Slice-level random split would leak adjacent slices and inflate Dice ~5 points. |
+| **Source** | [ACDC Challenge (MICCAI 2017)](https://www.creatis.insa-lyon.fr/Challenge/acdc/) |
+| **Reference** | Bernard et al., *IEEE TMI 2018* |
+| **License** | Original ACDC license; please refer to the original challenge website. |
+
+### 2. BraTS 2020 — Brain tumor MRI (FLAIR slices)
+
+| | |
+|---|---|
+| **Modality** | Brain MRI, FLAIR channel |
+| **Original task** | Multi-class tumor segmentation |
+| **Volumes / slices** | 369 / 22677 (this release: FLAIR-only 2D slices) |
+| **Mask convention here** | Whole-tumor binary (label 1+2+4 → 1) |
+| **Split type (canonical)** | **Volume-level**, 295 train / 74 test, seed=42 |
+| **Split file** | `BraTS2020/split_info.json` |
+| **Leakage risk** | None at volume level. Slice-level random would leak adjacent slices ~5 Dice points. |
+| **Note** | Only the FLAIR modality is included. The original BraTS release has T1/T1ce/T2 in addition. If you need multi-modal data, fetch the original release. |
+| **Source** | [BraTS 2020 Challenge](https://www.med.upenn.edu/cbica/brats2020/) |
+| **Reference** | Menze et al., *IEEE TMI 2015*; Bakas et al., 2017 |
+| **License** | Original BraTS license; please refer to the challenge website. |
+
+### 3. BUSI — Breast ultrasound
+
+| | |
+|---|---|
+| **Modality** | B-mode breast ultrasound |
+| **Original task** | Lesion segmentation (benign / malignant / normal classes are also available) |
+| **Images** | 780 |
+| **Mask convention** | Binary foreground; mask filenames carry `_mask.png` suffix |
+| **Split type (canonical)** | Image-level, 80/20, seed=42 |
+| **Leakage risk** | ⚠️ The release does **not** publish patient IDs. Multiple images may come from the same patient. The image-level split is the community standard; "patient-level" cannot be verified from the release. |
+| **Source** | [BUSI Dataset (Cairo University)](https://scholar.cu.edu.eg/?q=afahmy/pages/dataset) |
+| **Reference** | Al-Dhabyani et al., *Data in Brief 2020* |
+| **License** | CC-BY-4.0 |
+
+### 4. CVC-ClinicDB — Colonoscopy polyp
+
+| | |
+|---|---|
+| **Modality** | Colonoscopy (RGB endoscopy) |
+| **Original task** | Polyp segmentation |
+| **Frames / video sequences** | 612 / 29 |
+| **Mask convention** | Binary polyp foreground |
+| **Split type (canonical)** | **Video-level**, 23 train / 6 test sequences, seed=42 (489 frames train, 123 frames test) |
+| **Split file** | `CVC-ClinicDB/video_split_seed42.json` |
+| **Image-level split** | Available *for backward compatibility with prior literature*, but **leaks same-video frames** across train/test — can inflate Dice ~20–24 points. Image-level is the convention in many older papers; if you must reproduce them, use their split, but understand it is leaky. |
+| **Recommendation** | New work should use the video-level split. |
+| **Source** | [CVC-ClinicDB](https://polyp.grand-challenge.org/CVCClinicDB/) |
+| **Reference** | Bernal et al., *Computerized Medical Imaging and Graphics 2015* |
+| **License** | Released for academic use; cite the original paper. |
+
+### 5. Kvasir-SEG — Colonoscopy polyp
+
+| | |
+|---|---|
+| **Modality** | Colonoscopy (RGB endoscopy) |
+| **Original task** | Polyp segmentation |
+| **Images** | 1000 |
+| **Mask convention** | Binary polyp foreground |
+| **Split type (canonical)** | Image-level, 80/20, seed=42 |
+| **Leakage risk** | The release does not publish per-procedure metadata. Image-level is the community standard. |
+| **Note** | Auxiliary `bbox/` (bounding boxes) included from the original release. |
+| **Source** | [Kvasir-SEG](https://datasets.simula.no/kvasir-seg/) |
+| **Reference** | Jha et al., *MMM 2020* |
+| **License** | CC-BY-4.0 |
+
+### 6. REFUGE2 — Fundus optic disc
+
+| | |
+|---|---|
+| **Modality** | Fundus photography |
+| **Original task** | Optic disc and cup segmentation |
+| **Images** | 400 (across train/val/test as released) |
+| **Mask convention** | Multi-class (BG / disc / cup) preserved; for binary disc segmentation, treat any non-background pixel as foreground |
+| **Split type** | Pre-released train/val/test split is preserved |
+| **Leakage risk** | None — each image is from a different patient by protocol. |
+| **Caveat** | Modern segmenters reach ≥99.9 Dice on optic-disc segmentation; this dataset is **saturated** for that task. Use only when you specifically need fundus / glaucoma data. |
+| **Source** | [REFUGE2 Challenge](https://refuge.grand-challenge.org/) |
+| **Reference** | Orlando et al., *Medical Image Analysis 2020*; Fang et al., *Medical Image Analysis 2022* |
+| **License** | Original REFUGE2 license; please refer to the challenge website. |
+
+### 7. ISIC 2018 — Dermoscopy
+
+| | |
+|---|---|
+| **Modality** | Dermoscopy |
+| **Original task** | Skin lesion segmentation (Task 1) |
+| **Images** | 2594 train + 100 val + 1000 test (this release: PNG-extracted from the original ISIC 2018 archive) |
+| **Mask convention** | Binary lesion foreground (any-pixel > 0 → 1) |
+| **Split type** | Pre-released train/validation/test split is preserved |
+| **Leakage risk** | The release does not publish patient IDs. Multiple lesions per patient are possible but cross-lesion contamination is generally considered low risk. |
+| **Source** | [ISIC 2018 Challenge](https://challenge.isic-archive.com/landing/2018) |
+| **Reference** | Codella et al., 2019; Tschandl et al., *Sci. Data 2018* |
+| **License** | CC-BY-NC-4.0 (HAM10000-derived images) |
+
+---
+
+## Recommended use
+
+**For paired-comparison evaluation across methods**, lock to the canonical
+splits in this release:
+
+```python
+import json, os
+from huggingface_hub import snapshot_download
+
+ROOT = snapshot_download("MaybeRichard/MedSeg-7D", repo_type="dataset")
+
+# ACDC (patient-level)
+info = json.load(open(os.path.join(ROOT, "ACDC", "split_info.json")))
+train_patients = set(info["train_patients"])
+# enumerate slices, check patient ID in filename to assign train/test
+
+# BraTS (volume-level) — same pattern
+info = json.load(open(os.path.join(ROOT, "BraTS2020", "split_info.json")))
+train_volumes = set(info["train_patients"])  # key name retained from original
+
+# CVC (video-level — recommended) or image-level (legacy)
+info = json.load(open(os.path.join(ROOT, "CVC-ClinicDB", "video_split_seed42.json")))
+train_seqs = set(info["train_sequences"])
+```
+
+For datasets without a `split_info.json`, the canonical image-level split
+is reproducible from `seed=42`:
+
+```python
+import numpy as np
+def get_image_level_split(n_images, seed=42, train_ratio=0.8):
+    perm = np.random.RandomState(seed).permutation(n_images)
+    n_train = int(n_images * train_ratio)
+    return perm[:n_train], perm[n_train:]
+```
+
+---
+
+## Known caveats and good practices
+
+1. **Never use slice-level random split for ACDC or BraTS.** Same-patient
+   adjacent slices end up on both sides and inflate Dice ~5 points.
+   Always read `split_info.json`.
+
+2. **CVC image-level split is leaky.** Same-video frames cross train/test.
+   Use the video-level split (`video_split_seed42.json`) for clean
+   evaluation. Use image-level only for direct comparison to legacy
+   literature, and label such results as "leakage-audited / auxiliary".
+
+3. **BUSI / Kvasir / ISIC do not provide patient IDs.** Image-level random
+   is the de-facto community standard; do not claim "patient-level
+   independent" — there is no metadata to verify it.
+
+4. **REFUGE2 saturates at ~99.9 Dice.** Don't use it as a downstream
+   evaluator for augmentation studies; use it only when you need a
+   fundus / optic-disc task specifically.
+
+5. **Mask conventions vary across datasets.** Some are multi-class
+   (ACDC: 4 classes; BraTS original: 4 classes; REFUGE2: 3 classes).
+   For binary segmentation, use `mask > 0`. The released masks here
+   keep the original multi-class labels where applicable (no
+   information lost), so users can choose to binarize as needed.
+
+6. **All images and masks are at original resolution.** No
+   pre-processing baked in; you can resize per your protocol.
+
+---
+
+## Citation
+
+If this release is useful, please cite both the original dataset papers
+(see per-dataset references above) and the evaluation-protocol audit that
+produced these canonical splits:
+
+```bibtex
+@inproceedings{medseg7d2026,
+  title  = {An Evaluation-Protocol Audit of Pixel- vs.\ Latent-Space Diffusion
+            Augmentation for Medical Image Segmentation},
+  author = {Anonymous},
+  booktitle = {NeurIPS 2026 (E\&D Track)},
+  year   = {2026}
+}
+```
+
+## License
+
+This release does **not** redistribute datasets that are not already
+publicly available. Each dataset retains its original license; consult
+each per-dataset section above. The split metadata files
+(`split_info.json`, `video_split_seed42.json`) are released under MIT.