Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations
This addresses a critical gap in survival analysis for cancer research by handling incomplete modalities and labels, though it appears incremental by building on prior work on missing modalities.
The paper tackles the problem of incomplete data and censored labels in multi-modal survival analysis for cancer treatment, introducing a framework that uses foundation models and pseudo labels to achieve outstanding prediction accuracy on two tasks across datasets.
Survival analysis stands as a pivotal process in cancer treatment research, crucial for predicting patient survival rates accurately. Recent advancements in data collection techniques have paved the way for enhancing survival predictions by integrating information from multiple modalities. However, real-world scenarios often present challenges with incomplete data, particularly when dealing with censored survival labels. Prior works have addressed missing modalities but have overlooked incomplete labels, which can introduce bias and limit model efficacy. To bridge this gap, we introduce a novel framework that simultaneously handles incomplete data across modalities and censored survival labels. Our approach employs advanced foundation models to encode individual modalities and align them into a universal representation space for seamless fusion. By generating pseudo labels and incorporating uncertainty, we significantly enhance predictive accuracy. The proposed method demonstrates outstanding prediction accuracy in two survival analysis tasks on both employed datasets. This innovative approach overcomes limitations associated with disparate modalities and improves the feasibility of comprehensive survival analysis using multiple large foundation models.