arXiv (All)
open access
Conserved Immune Topology Improves Pathology Foundation Model Generalization for Cross-Cancer MSI-H Prediction
Pathology foundation models integrated with multiple instance learning achieve competitive accuracy within single-cancer cohorts, yet cross-cancer generalization remains unresolved due to organ-specific histological and architectural differences. In this paper, we propose Conserved Immune Topology (
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Domain Adaptation for Breast Cancer Computational Pathology: Evaluating Feature-Level and Pixel-Level Approaches Under Leave-One-Domain-Out Framework#215387
- Development of machine learning and nomogram models to predict lung metastasis and prognosticate survival in breast cancer.#258173
- Artificial intelligence-powered H&E-based quantification of spatial tumor-infiltrating lymphocyte distribution identifies prognostic immune niches in colorectal cancer.#156980
- Predicting Metastatic Risk from Primary Cancer Tissue Architecture via Distance-Aware Spatial Modeling#800089
- Enhanced MobileNet with multi-scale feature fusion for automated breast cancer histopathology classification.#375270
Record · ID 972127
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive