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MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention — arXiv:2609.21811

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21811
License (as archived): CC0-1.0
Archived: 2026-10-10T01:05:35.492Z
SHA-256 of the archived text: 7f4434707c2dc9cac7c5682f3c392144ecd3fb8302c45c6beffe9d2bdb1c4b4e
Stable URL of this page: https://metraipolis.com/documento/dc_ebb1f91bda2e

How to cite

MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention — arXiv:2609.21811 — arXiv (en), https://arxiv.org/abs/2609.21811 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ebb1f91bda2e (Metraipolis archive dc_ebb1f91bda2e, sha256 7f4434707c2dc9cac7c5682f3c392144ecd3fb8302c45c6beffe9d2bdb1c4b4e)

BibTeX

@misc{dcebb1f91bda2e,
  title = {MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention — arXiv:2609.21811},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21811},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ebb1f91bda2e; sha256 7f4434707c2dc9cac7c5682f3c392144ecd3fb8302c45c6beffe9d2bdb1c4b4e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21811. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.21811 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T00:23:42.477Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).