CIGTSurv uses clinical text embeddings to guide cross-attention and distribution alignment between pathology and genomics, reaching an average C-index of 0.788 across five TCGA cohorts.
Feature re-embedding: Towards foundation model-level performance in computa- tional pathology
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CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment
CIGTSurv uses clinical text embeddings to guide cross-attention and distribution alignment between pathology and genomics, reaching an average C-index of 0.788 across five TCGA cohorts.