Robust Multimodal Fusion for Survival Prediction in Cancer Patients.

ABSTRACT:

Objectives

Multimodal deep learning models have the potential to significantly improve survival predictions and treatment planning for cancer patients. These models integrate diverse data modalities using early, intermediate, or late fusion techniques. However, many existing multimodal models either underperform or show only marginal improvements over unimodal models. To establish the true efficacy of multimodal survival prediction models, it is critical to demonstrate consistent and substantial advantages over unimodal counterparts.

Methods

In this paper, we introduce the Robust Multimodal Survival Model (RMSurv), a novel discrete late fusion model that leverages synthetic data generation to compute time-dependent weights for various modalities. RMSurv utilizes up to 6 dist

SUBMITTER: Flack D 

PROVIDER: S-EPMC12476512 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature