HC-MAE: Hierarchical Cross-attention Masked Autoencoder Integrating Histopathological Images and Multi-omics for Cancer Survival Prediction

Accurate cancer survival prediction enables clinicians to tailor treatment regimens based on individual patient prognoses, effectively mitigating over-treatment and inefficient medical resource allocation. Recently, the integration of histopathological images and multi-omics data, together with deep learning, has become increasingly applied to predict cancer survival. However, current deep learning-based integration methods ignore the spatial relationships across various fields of view within gigapixel histopathological images, since they mainly focus on a specific field of view. Inspired by the hierarchical image pyramid transformer (HIPT), we propose a hierarchical cross-attention masked autoencoder (HC-MAE) to integrate histopathological images and multi-omics data for cancer survival prediction. Specifically, HC-MAE aggregates the representations learned from different fields of view, effectively capturing the fine-grained details and the spatial relationships within histopathological images. We conduct experiments to compare the HC-MAE method with current state-of-the-art methods on six cancer datasets sourced from The Cancer Genome Atlas (TCGA). The experimental results demonstrate that HC-MAE achieves superior performance on five out of six cancer datasets, significantly outperforming the compared methods. The code is available at https://github.com/SuixueWang/HC-MAE.