Dual-stream cross-modal fusion alignment network for survival analysis.

ABSTRACT: Survival prediction serves as a pivotal component in precision oncology, enabling the optimization of treatment strategies through mortality risk assessment. While the integration of histopathological images and genomic profiles offers enhanced potential for patient stratification, existing methodologies are constrained by two fundamental limitations: (i) insufficient attention to fine-grained local features in favor of global representations, and (ii) suboptimal cross-modal fusion strategies that either neglect intrinsic correlations or discard modality-specific information. To address these challenges, we propose DSCASurv, a novel cross-modal fusion alignment framework designed to explore and integrate intrinsic correlations across multimodal data, thereby improving the accuracy of survi

SUBMITTER: Song J 

PROVIDER: S-EPMC11926988 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature