Supervised Contrastive Pre-training forMammographic Triage Screening Models

Inspired by the recent success of self-supervised contrastive pre-training on ImageNet, this paper presents a novel framework of Supervised Contrastive Pre-training (SCP) followed by Supervised Fine-tuning (SF) to improve mammographic triage screening models. Our experiments on a large-scale dataset show that the SCP step can effectively learn a better embedding and subsequently improve the final model performance in comparison with the direct supervised training approach. Superior results of AUC and specificity/sensitivity have been achieved for our mammographic screening task compared to previously reported SOTA approaches.