BIS5k: a large-scale dataset for medical segmentation task based on HE-staining images of breast cancer

Breast cancer, a high-incidence cancer among female, occupies a large incidence of total female patients with cancer. Pathological examination is the gold standard for breast cancer in clinic diagnosis. However, accuracy and efficient diagnosis is challengeable to pathologists for the complex of breast cancer and laborious work. Introducing computer-aid diagnosis (CAD) can relieve laborious work of pathologists and improve diagnosed accuracy for breast cancer. To promote development of CAD methods, we release a large-scale and hematoxylin-eosin (HE) staining dataset of breast cancer for medical image segmentation task, called the breast-cancer image segmentation 5000 (BIS5k). BIS5k contains 5929 images that are divided into training data (5000) and evaluated data (929). All images of BIS5k are collected from clinic cases which include patients with various age and cancer stages. All labels of images are annotated in pixel level for segmentation task and reviewed by pathological professors carefully. Furthermore, we construct a basic instance called breast-cancer segmentation network, BCSNet with a toolkit including comprehensive metrics to demonstrate the usage of BIS5k. Extensive experiments of BCSNet and compared methods provide that developing specific algorithm and constructing dataset are indispensable to promote CAD of pathological diagnosis for breast cancer.