Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction.

Abstract

Early breast cancer screening through mammography produces every year millions of images worldwide. Despite the volume of the data generated, these images are not systematically associated with standardized labels. Current protocols encourage giving a malignancy probability to each studied breast but do not require the explicit and burdensome annotation of the affected regions. In this work, we address the problem of abnormality detection in the context of such weakly annotated datasets. We combine domain knowledge about the pathology and clinically available image-wise labels to propose a mixed self- and weakly supervised learning framework for abnormalities reconstruction. We also introduce an auxiliary classification task based on the reconstructed regions to improve explainability. We work with high-resolution imaging that enables our network to capture different findings, including masses, micro-calcifications, distortions, and asymmetries, unlike most state-of-the-art works that mainly focus on masses. We use the popular INBreast dataset as well as our private multi-manufacturer dataset for validation and we challenge our method in segmentation, detection, and classification versus multiple state-of-the-art methods. Our results include image-wise AUC up to 0.86, overall region detection true positives rate of 0.93, and the pixel-wise F₁ score of 64% on malignant masses.