AI RESEARCH
Attention-Based Multimodal Survival Prediction with Cross-Modal Bilinear Fusion
arXiv CS.LG
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ArXi:2605.13897v1 Announce Type: cross We propose a novel multimodal deep learning framework for patient-level survival prediction, which integrates whole-slide histology features, RNA-seq expression profiles, and clinical variables. Our architecture combines an ABMIL module~\cite{ilse2018attention} for slide-level representation with feedforward encoders for RNA and clinical data. These embeddings are then integrated through low-rank bilinear cross-modal fusion~\cite{liu2018efficient} to model conditional interactions across modalities while controlling parameter growth.