Hierarchical Attention Guided Framework for Multi-resolution Collaborative Whole Slide Image Segmentation

Segmentation of whole slide images (WSIs) is an important step for computer-aided cancer diagnosis. However, due to the gigapixel dimension, WSIs are usually cropped into patches for analysis. Processing high-resolution patches independently may leave out the global geographical relationships and suffer slow inference speed while using low-resolution patches can enlarge receptive fields but lose local details. Here, we propose a Hierarchical Attention Guided (HAG) framework to address above problems. Particularly, our framework contains a global branch and several local branches to perform prediction at different scales. Additive hierarchical attention maps are generated by the global branch with sparse constraints to fuse multi-resolution predictions for better segmentation. During the inference, the sparse attention maps are used as the certainty guidance to select important local areas with a quadtree strategy for acceleration. Experimental results on two WSI datasets highlight two merits of our framework: 1) effectively aggregate multi-resolution information to achieve better results, 2) significantly reduce the computational cost to accelerate the prediction without decreasing accuracy.