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 GitHub repo and rebuilt here from the 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

@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}
}