  In pathology, whole-slide images (WSI) based survival prediction has
attracted increasing interest. However, given the large size of WSIs and the
lack of pathologist annotations, extracting the prognostic information from
WSIs remains a challenging task. Previous studies have used multiple instance
learning approaches to combine the information from multiple randomly sampled
patches, but different visual patterns may contribute differently to prognosis
prediction. In this study, we developed a multi-head attention approach to
focus on various parts of a tumor slide, for more comprehensive information
extraction from WSIs. We evaluated our approach on four cancer types from The
Cancer Genome Atlas database. Our model achieved an average c-index of 0.640,
outperforming two existing state-of-the-art approaches for WSI-based survival
prediction, which have an average c-index of 0.603 and 0.619 on these datasets.
Visualization of our attention maps reveals each attention head focuses
synergistically on different morphological patterns.
