Mammographic Breast Composition Classification Using Swin Transformer Network

Breast cancer is a prevalent global health concern and the most commonly diagnosed cancer in women. Mammography, a well -established and widely used screening tool, has greatly contributed to early breast cancer detection. However, understanding mammographic breast composition is also crucial for refining the risk assessment of breast cancer beyond identifying lesions. In contrast to previous studies, we adopt an exploratory approach by using the Swin Transformer, a foundation model for image classification, to classify the four -category breast density. Leveraging this foundation, we fine-tune the model with a small set of mammograms for the purpose of making advancements in breast density classification. This study is experimentally validated to achieve an overall accuracy of 74.96% in the four -category breast density classification, which is a comparable performance to recent counterparts.