Multimodal evidence is critical in computational pathology: gigapixel whole slide images capture tumor morphology, while patient-level clinical descriptors preserve complementary context for prognosis. Integrating such heterogeneous signals remains challenging because feature spaces exhibit distinct statistics and scales. We introduce MMSF, a multitask and multimodal supervised framework built on a linear-complexity MIL backbone that explicitly decomposes and fuses cross-modal information. MMSF comprises a graph feature extraction module embedding tissue topology at the patch level, a clinical data embedding module standardizing patient attributes, a feature fusion module aligning modality-shared and modality-specific representations, and a Mamba-based MIL encoder with multitask prediction heads. Experiments on CAMELYON16 and TCGA-NSCLC demonstrate 2.1--6.6\% accuracy and 2.2--6.9\% AUC improvements over competitive baselines, while evaluations on five TCGA survival cohorts yield 7.1--9.8\% C-index improvements compared with unimodal methods and 5.6--7.1\% over multimodal alternatives.
@article{arxiv.2601.20347,
title = {MMSF: Multitask and Multimodal Supervised Framework for WSI Classification and Survival Analysis},
author = {Chengying She and Chengwei Chen and Xinran Zhang and Ben Wang and Lizhuang Liu and Chengwei Shao and Yun Bian},
journal= {arXiv preprint arXiv:2601.20347},
year = {2026}
}
Comments
Submitted to "Biomedical Signal Processing and Control"