Detection of Metastatic Breast Cancer from Whole-Slide Pathology Images Using an Ensemble Deep-Learning Method

Breast cancer (BC) is the most prevalent cancer among women and raises a fundamental challenge in public health globally. Breast cancer continues to be the most commonly diagnosed cancer and the second leading cause of cancer deaths among U.S. women.۱ The rate of BC incidence shows no declining prospect and in ۲۰۲۱, an estimated ۲۸۱,۵۵۰ new cases of invasive BC were expected to be diagnosed in women in the U.S. including ۲۶۵۰ new invasive cases, with an estimated death toll of ۴۳۶۰۰ women.۲ Despite our profound understanding of biological mechanisms behind BC progression that led to development of various diagnostic and therapeutic approaches, the widespread incidences of BC and its subsequent heavy tolls necessitate early and accurate detection of BC. Besides, the emergence of personalized medicine drastically increases the load of work for pathologists and further complicates the histopathologic detection of cancer. Therefore, it is important that diagnostic protocols equally concentrate on the accuracy and efficiency of their performance. Recognition of lymph node metastases (LNMets)