Localization, segmentation, and classification of mammographic abnormalities using deep learning

Breast cancer is a disease caused by abnormal growth of cells in the breast. We have investigated a deep learning pipeline, which provides classification (e.g. normal/ abnormal), and subsequently localization and segmentation of abnormalities. We have used the digital database for screening mammography in this work. The contributions of this paper are two-fold. First, we classify between normal and abnormal mammograms with a 100% training and 98.34% testing accuracy. Second, a framework is proposed to localize and segment abnormalities from abnormal images with a training loss of 0.57 and a testing loss of 0.55 where the multi-task loss function combines the loss of classification, localization, and segmentation mask.