BEETLE: A multicentric dataset for training and benchmarking breast cancer segmentation in H&E slides

The BrEast cancEr hisTopathoLogy sEgmentation (BEETLE) dataset provides a development set and an external evaluation set for multiclass semantic segmentation of H&E-stained breast cancer whole-slide images (WSIs), covering all molecular subtypes and histological grades. Development set: 587 biopsies and resections collected from three collaborating clinical centers and two public datasets, digitized using seven scanners. Pixel-level annotations are available for four tissue classes: invasive epithelium, non-invasive epithelium, necrosis, and other, with particular focus on morphologies underrepresented in existing datasets, such as ductal carcinoma in situ and dispersed lobular tumor cells. External evaluation set: 54 biopsies and resections collected from three clinical centers and digitized with three scanners. In addition to the WSIs, 170 densely annotated regions of interest (ROIs) are provided as image tiles. The corresponding pixel-level annotations are not publicly released but are sequestered on the Grand Challenge platform, where submissions are evaluated on a public leaderboard to enable standardized and comparable benchmarking of breast cancer segmentation models.