Add dataset card

README.md

@@ -1,27 +1,88 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: image_id
-    dtype: string
-  - name: patient_id
-    dtype: string
-  - name: xmin
-    dtype: int32
-  - name: ymin
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 4910314207
-    num_examples: 151
-  download_size: 4910506566
-  dataset_size: 4910314207
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: cc0-1.0
+task_categories:
+  - image-segmentation
+tags:
+  - medical
+  - pathology
+  - h-and-e
+  - breast
+  - segmentation
+  - bcss
+size_categories:
+  - n<1K
 ---
+
+# BCSS — Breast Cancer Semantic Segmentation (Amgad et al. 2019)
+
+Re-hosted mirror of the **Breast Cancer Semantic Segmentation** dataset
+(Amgad et al., *Bioinformatics* 2019), originally distributed via the
+[PathologyDataScience/BCSS](https://github.com/PathologyDataScience/BCSS)
+GitHub repo and rebuilt here from the
+[`nabil-m/bcss`](https://huggingface.co/datasets/nabil-m/bcss) HF mirror.
+
+The data is **CC0 1.0** (public domain, no rights reserved); the upstream
+codebase is MIT-licensed but covers software, not data. Redistribution is
+unrestricted.
+
+## Composition
+
+| Split | ROIs |
+|-------|-----:|
+| train | 151  |
+
+151 ROI patches extracted from TCGA breast cancer whole-slide images.
+Patches are **color-normalized** RGB at the upstream MPP=0.25 µm/px
+(40× equivalent), with native ROI resolution typically 2–4k px per
+side. There is **no official train/val/test split** — group-shuffle by
+`patient_id` downstream for honest evaluation.
+
+## Schema
+
+| Column      | Type     | Description                                           |
+|-------------|----------|-------------------------------------------------------|
+| `image`     | `Image`  | RGB ROI (PNG, color-normalized, variable size)        |
+| `mask`      | `Image`  | Indexed 22-class mask (`L`, values 0..21)             |
+| `image_id`  | `string` | Filename stem incl. xmin/ymin                         |
+| `patient_id`| `string` | TCGA-XX-YYYY prefix                                   |
+| `xmin`      | `int32`  | ROI bbox xmin in WSI base-magnification pixels        |
+| `ymin`      | `int32`  | ROI bbox ymin in WSI base-magnification pixels        |
+
+## Mask labels
+
+| Code | Class                  | | Code | Class                |
+|-----:|------------------------|-|-----:|----------------------|
+| 0    | outside_roi (don't care) | | 11   | other_immune_infiltrate |
+| 1    | tumor                  | | 12   | mucoid_material      |
+| 2    | stroma                 | | 13   | normal_acinus_or_duct |
+| 3    | lymphocytic_infiltrate | | 14   | lymphatics           |
+| 4    | necrosis_or_debris     | | 15   | undetermined         |
+| 5    | glandular_secretions   | | 16   | nerve                |
+| 6    | blood                  | | 17   | skin_adnexa          |
+| 7    | exclude                | | 18   | blood_vessel         |
+| 8    | metaplasia_NOS         | | 19   | angioinvasion        |
+| 9    | fat                    | | 20   | dcis                 |
+| 10   | plasma_cells           | | 21   | other                |
+
+**Code 0 (`outside_roi`) is a "don't care" region** — the original paper
+recommends excluding it from any loss. For binary tumor evaluation,
+the canonical foreground is class 1.
+
+## License
+
+CC0 1.0 Universal — public domain. No rights reserved.
+
+## Citation
+
+```bibtex
+@article{amgad2019structured,
+  title   = {Structured crowdsourcing enables convolutional segmentation of histology images},
+  author  = {Amgad, Mohamed and Elfandy, Habiba and Hussein, Hagar and others},
+  journal = {Bioinformatics},
+  volume  = {35},
+  number  = {18},
+  pages   = {3461--3467},
+  year    = {2019},
+  doi     = {10.1093/bioinformatics/btz083}
+}
+```