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 this https URL

本文提出了一种创新的基于 Transformer 的多视图网络，用于解决乳房 X 线照片分类中的挑战。我们的方法通过引入一种新颖的基于移动窗口的动态注意力块，促进多视图信息的有效集成，并在空间特征映射级别上促进该信息在视图之间的一致传递。此外，我们使用 CBIS-DDSM 和 Vin-Dr Mammo 数据集对基于 Transformer 的模型在不同设置下的性能和有效性进行了全面的比较分析。我们的代码公开可用于此 https URL。