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
context rather than merging separately encoded modalities only at
the final stage. Training combines discrete-time survival prediction with
genomic feature masking, WSI dropout, and paired WSI–genomics contrastive
alignment. Across four external evaluations in colon, renal, lung,
and glioblastoma cohorts, MIST improves external C-index over standard
fusion baselines in the primary comparisons. These results support
genomic-guided histology attention as a compact and effective strategy
for multimodal oncology outcome prediction. Our code is available at
https://github.com/samiyavuuz/MIST.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/COMPAYL_072.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
BibTex
@InProceedings{YavMuh_MIST_MICCAISAT2026,
author = { Yavuz, Muhammet Sami AND Kahya, Sabri Mustafa AND Chen, Richard R. AND Lipkova, Jana AND Wiestler, Benedikt},
title = { { MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17251},
month = {pending},
page = {pending}
}