MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention
Quick summary
arXiv:2609.21811v1 Announce Type: new Abstract: Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external cohort shift and computational complexity. To address these challenges, we propose MIST, multimodal survival prediction with genomic-guided histology attention. MIST represents genomic features as tokens and allows them to query compact foundation-model-derived histology context tokens before survival prediction. This design enriches molecular information with histology co
Key takeaways
- arXiv:2609.21811v1 Announce Type: new Abstract: Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external cohort shift and computational complexity.
- To address these challenges, we propose MIST, multimodal survival prediction with genomic-guided histology attention.
- MIST represents genomic features as tokens and allows them to query compact foundation-model-derived histology context tokens before survival prediction.
Why it matters
“MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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