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In pathology, whole-slide images (WSI) based survival prediction hasattracted increasing interest. However, given the large size of WSIs and thelack of pathologist annotations, extracting the prognostic information fromWSIs remains a challenging task. Previous studies have used multiple instancelearning approaches to combine the information from multiple randomly sampledpatches, but different visual patterns may contribute differently to prognosisprediction. In this study, we developed a multi-head attention approach tofocus on various parts of a tumor slide, for more comprehensive informationextraction from WSIs. We evaluated our approach on four cancer types from TheCancer Genome Atlas database. Our model achieved an average c-index of 0.640,outperforming two existing state-of-the-art approaches for WSI-based survivalprediction, which have an average c-index of 0.603 and 0.619 on these datasets.Visualization of our attention maps reveals each attention head focusessynergistically on different morphological patterns.

MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images