Unsupervised feature correlation network for localizing breast cancer using history of mammograms

Automatic cancer localization of irregular shaped abnormalities from mammogram images has remained challenging. This is mainly because annotated mammograms are scarce for training learning models to analyze such high resolution and complex images. In clinical settings for mammogram screening, radiologists not only examine images obtained during the examination, but also compare the current and prior mammogram images to make a clinical decision. To have an automatic breast cancer localization system and to address the problem of lack of annotated mammograms, in this study, we develop an unsupervised feature correlation deep learning model. The proposed model compares unannotated current and previous mammograms and employs an attention-based U-Net based network to identify and generate a map for abnormalities.