Single-Domain Generalization for Multimodal Cross-Cancer Prognosis via Dirac Rebalancer and Distribution Entanglement
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
Deep learning has shown remarkable performance in integrating multimodal data
for survival prediction. However, existing multimodal methods mainly focus on
single cancer types and overlook the challenge of generalization across
cancers. In this work, we are the first to reveal that multimodal prognosis
models often generalize worse than unimodal ones in cross-cancer scenarios,
despite the critical need for such robustness in clinical practice. To address
this, we propose a new task: Cross-Cancer Single Domain Generalization for
Multimodal Prognosis, which evaluates whether models trained on a single cancer
type can generalize to unseen cancers. We identify two key challenges: degraded
features from weaker modalities and ineffective multimodal integration. To
tackle these, we introduce two plug-and-play modules: Sparse Dirac Information
Rebalancer (SDIR) and Cancer-aware Distribution Entanglement (CADE). SDIR
mitigates the dominance of strong features by applying Bernoulli-based
sparsification and Dirac-inspired stabilization to enhance weaker modality
signals. CADE, designed to synthesize the target domain distribution, fuses
local morphological cues and global gene expression in latent space.
Experiments on a four-cancer-type benchmark demonstrate superior
generalization, laying the foundation for practical, robust cross-cancer
multimodal prognosis. Code is available at
https://github.com/HopkinsKwong/MCCSDG
