输入关键词或点击标签，按论文、数据资源、竞赛截止日期、征稿与课程缩小范围。 标签：breast cancer
CAMELYON17 is a digital pathology dataset for detecting breast cancer metastases in lymph node whole-slide images across multiple centers. It supports pathology classification, metastasis detection, weakly supervised learning, and domain generalization in histopathology AI.
Grand Challenge official API lists this medical AI challenge with status OPEN. BEETLE is a multicenter, multiscanner benchmark for breast cancer histopathology segmentation. It focuses on multiclass semantic segmentation of hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) into four tissue categories: invasive epithelium, non-invasive epithelium, necrosis, and other. The evaluation set comprises 170 densely annotated regions from 54 WSIs, covering all molecular subtypes and histological grades, thereby capturing much of the morphological heterogeneity seen in clinical practice. BEETLE provides a standardized resource for benchmarking breast cancer segmentation models, supporting the development of robust, generalizable algorithms for large-scale biomarke...