面向数据高效精准肿瘤学的病理组学多模态结构表征学习
ICLR 2026 Poster accepted paper at ICLR 2026. Fusing histopathology images and genomics data with deep learning has significantly advanced precision oncology. However, genomics data is often missing due to its high acquisition cost and complexity in real-world clinical scenarios. Existing solutions aim to reconstruct genomics data from histopathology images. Nevertheless, these methods typically relied only on individual case and overlooked the potential relationships among cases. Additionally, they failed to take advantage of the authentic genomics data of diagnostically related cases that are accessible from training for inference. In this work, we propose a novel Multi-modal Structural Representation Learning (MSRL) framework for data-efficient precision oncology. Code/project link: https://github.com/WkEEn/MSRL