RobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction

TL;DR

RobSurv introduces a vector quantization-based deep learning framework that enhances robustness and accuracy in multi-modal cancer survival prediction across diverse datasets and noisy conditions.

Contribution

The paper presents RobSurv, a novel dual-path architecture with vector quantization and patch-wise fusion, improving robustness and generalization in multi-modal medical imaging for cancer prognosis.

Findings

Achieves higher concordance indices than existing methods.

Maintains performance under severe noise conditions.

Demonstrates strong cross-dataset generalization.

Abstract

Cancer survival prediction using multi-modal medical imaging presents a critical challenge in oncology, mainly due to the vulnerability of deep learning models to noise and protocol variations across imaging centers. Current approaches struggle to extract consistent features from heterogeneous CT and PET images, limiting their clinical applicability. We address these challenges by introducing RobSurv, a robust deep-learning framework that leverages vector quantization for resilient multi-modal feature learning. The key innovation of our approach lies in its dual-path architecture: one path maps continuous imaging features to learned discrete codebooks for noise-resistant representation, while the parallel path preserves fine-grained details through continuous feature processing. This dual representation is integrated through a novel patch-wise fusion mechanism that maintains local spatial relationships while capturing global context via Transformer-based processing. In extensive evaluations across three diverse datasets (HECKTOR, H\&N1, and NSCLC Radiogenomics), RobSurv demonstrates superior performance, achieving concordance index of 0.771, 0.742, and 0.734 respectively - significantly outperforming existing methods. Most notably, our model maintains robust performance even under severe noise conditions, with performance degradation of only 3.8-4.5\% compared to 8-12\% in baseline methods. These results, combined with strong generalization across different cancer types and imaging protocols, establish RobSurv as a promising solution for reliable clinical prognosis that can enhance treatment planning and patient care.