BACH: Grand Challenge on Breast Cancer Histology Images

Breast cancer is the most common invasive cancer in women, aecting more than 10%of women worldwide. Microscopic analysis of a biopsy remains one of the most importantmethods to diagnose the type of breast cancer. This requires specialized analysisby pathologists, in a task that i) is highly time- and cost-consuming and ii) oftenleads to nonconsensual results. The relevance and potential of automatic classificationalgorithms using hematoxylin-eosin stained histopathological images has alreadybeen demonstrated, but the reported results are still sub-optimal for clinical use. Withthe goal of advancing the state-of-the-art in automatic classification, the Grand Challengeon BreAst Cancer Histology images (BACH) was organized in conjunction withthe 15th International Conference on Image Analysis and Recognition (ICIAR 2018).BACH aimed at the classification and localization of clinically relevant histopathologicalclasses in microscopy and whole-slide images from a large annotated dataset,specifically compiled and made publicly available for the challenge. Following a positiveresponse from the scientific community, a total of 64 submissions, out of 677registrations, eectively entered the competition. The submitted algorithms improvedthe state-of-the-art in automatic classification of breast cancer with microscopy imagesto an accuracy of 87%. Convolutional neuronal networks were the most successfulmethodology in the BACH challenge. Detailed analysis of the collective results allowedthe identification of remaining challenges in the field and recommendations for futuredevelopments. The BACH dataset remains publicly available as to promote further improvementsto the field of automatic classification in digital pathology.