Gated Spatial Redundancy Projection for Pathology Transformer Attentions
This work addresses the challenge posed by high spatial redundancy among neighboring patches in whole-slide pathological images, which can cause Transformer self-attention mechanisms to over-mix local features and obscure critical diagnostic signals. To mitigate this, the authors propose Gated SRP, a lightweight, plug-and-play module that estimates a local redundancy direction for each patch and attention head within the self-attention layer, projects the output onto this direction, and applies a learnable sign-gated geometric correction. This is the first attention modulation mechanism explicitly designed for the spatial redundancy inherent in histopathology images, introducing only a 0.02% parameter overhead while effectively preserving discriminative information. Experiments demonstrate that the method achieves the highest average C-index across five TCGA survival cohorts, outperforms baselines on 12 of 16 metrics across five slide-level classification datasets, and attains state-of-the-art AUC on three of them.