{"id":10315,"date":"2026-10-07T06:55:09","date_gmt":"2026-10-07T06:55:09","guid":{"rendered":"https:\/\/ai-grid.org\/?p=10315"},"modified":"2026-10-07T07:56:50","modified_gmt":"2026-10-07T07:56:50","slug":"ai-for-precision-medicine","status":"publish","type":"post","link":"https:\/\/ai-grid.org\/en\/uncategorized\/ai-for-precision-medicine\/","title":{"rendered":"AI for Precision Medicine: From Foundation Models to Tumor Tissue Profiling in Computational Pathology"},"content":{"rendered":"
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Pathology is central to cancer diagnosis, biomarker assessment, and treatment decisions, but the analysis of tumor tissue is still largely manual, semi-quantitative, and subjective. AI can make pathology more quantitative and scalable, but challenges remain: whole-slide images are extremely large, expert labelling is costly, cancer subtypes are unevenly represented, and differences in staining and scanners can cause models to learn hospital-specific signatures instead of biological patterns.<\/p>

This workshop explores the robustness of pathology foundation models and the analysis of the tumor microenvironment using open-access, de-identified data.<\/span><\/p><\/div><\/div>

In the session<\/span><\/strong><\/div><\/div>

Participants will work through two hands-on parts using Python notebooks in the browser, including Google Colab or Molab.<\/p>

Part I \u2013 Robust foundation models:<\/strong> Participants will measure how strongly pathology foundation-model embeddings encode the medical centre rather than biological information, compare models, and prototype possible mitigations such as stain augmentation, feature normalisation, and batch correction.<\/span><\/p><\/div><\/div>

Part II \u2013 Tumor microenvironment insights:<\/strong> Participants will explore OpenTME readouts for 4,691 TCGA slides across eight cancer types to identify tumor-microenvironment patterns that distinguish cancer types or relate to clinical and molecular annotations.<\/p><\/div><\/div>

What to expect<\/strong><\/p><\/div><\/div>

Participants can expect an introduction to the AI challenges of digital pathology and current approaches to pathology foundation models. They will gain hands-on experience with foundation-model features and quantitative tissue analysis and receive working notebooks that they can reuse in their own research.<\/p><\/div><\/div>

Participant requirements<\/strong><\/p><\/div><\/div>

Participants should:<\/p><\/div><\/div>