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Learning good representation of giga-pixel level whole slide pathology images(WSI) for downstream tasks is critical. Previous studies employ multipleinstance learning (MIL) to represent WSIs as bags of sampled patches because,for most occasions, only slide-level labels are available, and only a tinyregion of the WSI is disease-positive area. However, WSI representationlearning still remains an open problem due to: (1) patch sampling on a higherresolution may be incapable of depicting microenvironment information such asthe relative position between the tumor cells and surrounding tissues, whilepatches at lower resolution lose the fine-grained detail; (2) extractingpatches from giant WSI results in large bag size, which tremendously increasesthe computational cost. To solve the problems, this paper proposes ahierarchical-based multimodal transformer framework that learns a hierarchicalmapping between pathology images and corresponding genes. Precisely, werandomly extract instant-level patch features from WSIs with differentmagnification. Then a co-attention mapping between imaging and genomics islearned to uncover the pairwise interaction and reduce the space complexity ofimaging features. Such early fusion makes it computationally feasible to useMIL Transformer for the survival prediction task. Our architecture requiresfewer GPU resources compared with benchmark methods while maintaining betterWSI representation ability. We evaluate our approach on five cancer types fromthe Cancer Genome Atlas database and achieved an average c-index of $0.673$,outperforming the state-of-the-art multimodality methods.

Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics