MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer
Sushmita Sarker Prithul Sarker George Bebis Alireza Tavakkoli
Traditional deep learning approaches for breast cancer classification has
predominantly concentrated on single-view analysis. In clinical practice,
however, radiologists concurrently examine all views within a mammography exam,
leveraging the inherent correlations in these views to effectively detect
tumors. Acknowledging the significance of multi-view analysis, some studies
have introduced methods that independently process mammogram views, either
through distinct convolutional branches or simple fusion strategies,
inadvertently leading to a loss of crucial inter-view correlations. In this
paper, we propose an innovative multi-view network exclusively based on
transformers to address challenges in mammographic image classification. Our
approach introduces a novel shifted window-based dynamic attention block,
facilitating the effective integration of multi-view information and promoting
the coherent transfer of this information between views at the spatial feature
map level. Furthermore, we conduct a comprehensive comparative analysis of the
performance and effectiveness of transformer-based models under diverse
settings, employing the CBIS-DDSM and Vin-Dr Mammo datasets. Our code is
publicly available at https://github.com/prithuls/MV-Swin-T