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Conserved Immune Topology Improves Pathology Foundation Model Generalization for Cross-Cancer MSI-H Prediction

Research Medical/Healthcare AI

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TL;DR - Conserved Immune Topology (CIT) augments pathology foundation-model embeddings with spatial immune descriptors to improve cross-cancer MSI-H prediction. It enables stronger zero-shot transfer across colorectal and stomach cancer cohorts without annotations or target-domain data.

  • CIT captures tertiary lymphoid structures, peritumoral immune reactions, multi-scale tumor-infiltrating lymphocyte density, and immune-tumor mixing.
  • Immune-associated image tiles are identified through unsupervised clustering of frozen foundation-model embeddings and spatial coordinates.
  • On zero-shot cross-cancer transfer, CIT raised TransMIL AUC from 0.6627 to 0.7161, a 0.0534 absolute gain (p=0.003).
  • Improvements held across all three tested multiple-instance learning aggregators despite scanner, site, and organ-specific distribution shifts.

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Conserved Immune Topology Improves Pathology Foundation Model Generalization for Cross-Cancer MSI-H Prediction

arXiv cs.CV Dasari Naga Raju 2026-09-04 arXiv:2609.05182
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-19 14:16:57.575152 UTC

TL;DR - Conserved Immune Topology (CIT) augments pathology foundation-model embeddings with spatial immune descriptors to improve cross-cancer MSI-H prediction. It enables stronger zero-shot transfer across colorectal and stomach cancer cohorts without annotations or target-domain data.

  • CIT captures tertiary lymphoid structures, peritumoral immune reactions, multi-scale tumor-infiltrating lymphocyte density, and immune-tumor mixing.
  • Immune-associated image tiles are identified through unsupervised clustering of frozen foundation-model embeddings and spatial coordinates.
  • On zero-shot cross-cancer transfer, CIT raised TransMIL AUC from 0.6627 to 0.7161, a 0.0534 absolute gain (p=0.003).
  • Improvements held across all three tested multiple-instance learning aggregators despite scanner, site, and organ-specific distribution shifts.
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