| --- |
| 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} |
| } |
| ``` |
| |
