Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer in Mammography

Kihyun You, Suho Lee, Kyuhee Jo, Eunkyung Park, Thijs Kooi, Hyeonseob Nam

Abstract: Radiologists consider fine-grained characteristics of mammograms as well as patient-specific information before making the final diagnosis. Recent literature suggests that a similar strategy works for Computer Aided Diagnosis (CAD) models; multi-task learning with radiological and patient features as auxiliary classification tasks improves the model performance in breast cancer detection. Unfortunately, the additional labels that these learning paradigms require, such as patient age, breast density, and lesion type, are often unavailable due to privacy restrictions and annotation costs. In this paper, we introduce a contrastive learning framework comprising a Lesion Contrastive Loss (LCL) and a Normal Contrastive Loss (NCL), which jointly encourage models to learn subtle variations beyond class labels in a self-supervised manner. The proposed loss functions effectively utilize the multi-view property of mammograms to sample contrastive image pairs. Unlike previous multi-task learning approaches, our method improves cancer detection performance without additional annotations. Experimental results further demonstrate that the proposed losses produce discriminative intra-class features and reduce false positive rates in challenging cases.