Classification of pure DCIS cases in breast ultrasound images by multiscale contrastive learning

Distinction between invasive breast cancers and non-invasive cancers is important for determination of treatment planning. The decision is generally made based on biopsy; however, if diagnosis can be predicted by imaging, it would be useful for timely treatment planning and biopsy sampling. The purpose of this study is to classify breast ultrasound images with invasive cancers and non-invasive cancers. The number of cases used in this study is 690 breast ultrasound images, including 584 invasive cancers and 106 ductal carcinomas in situ (DCIS). Since cases are highly imbalanced, the model was first pretrained for matched pairs and unmatched pairs with contrastive loss. The model is then fine-tuned for classification of invasive cancers and DCISs. Although accuracy is almost unchanged, the recall for DCIS cases was slightly improved. Classification of non-invasive cancers on ultrasound images can support prompt treatment planning and biopsy procedures.