In the realm of digital pathology, multi-magnification Multiple Instance
Learning (multi-mag MIL) has proven effective in leveraging the hierarchical
structure of Whole Slide Images (WSIs) to reduce information loss and redundant
data. However, current methods fall short in bridging the domain gap between
pretrained models and medical imaging, and often fail to account for spatial
relationships across different magnifications. Addressing these challenges, we
introduce the Concentric Dual Fusion Attention-MIL (CDFA-MIL) framework,which
innovatively combines point-to-area feature-colum attention and point-to-point
concentric-row attention using concentric patch. This approach is designed to
effectively fuse correlated information, enhancing feature representation and
providing stronger correlation guidance for WSI analysis. CDFA-MIL
distinguishes itself by offering a robust fusion strategy that leads to
superior WSI recognition. Its application has demonstrated exceptional
performance, significantly surpassing existing MIL methods in accuracy and F1
scores on prominent datasets like Camelyon16 and TCGA-NSCLC. Specifically,
CDFA-MIL achieved an average accuracy and F1-score of 93.7\% and 94.1\%
respectively on these datasets, marking a notable advancement over traditional
MIL approaches.

数字病理学中，Concentric Dual Fusion Attention-MIL (CDFA-MIL) 框架结合点到区域特征列注意力和点到点同心行注意力，使用同心补丁，有效融合相关信息，增强特征表示，并为 WSI 分析提供更强的相关性指导， 在准确性和 F1 分数上显着超越传统 MIL 方法，相应地在 Camelyon16 和 TCGA-NSCLC 等知名数据集上实现了平均准确率和 F1 得分分别为 93.7% 和 94.1% 的显著进展。