Multimodal Deep Learning & Spatial Analysis in Ovarian Cancer
- January 3, 2026
- Professional , Research
Overview This project focused on the development of a comprehensive computational pathology framework to investigate molecular signatures in ovarian cancer. The objective was to bridge the gap between unstructured histological data and patient-level clinical endpoints. The solution involved an automated pipeline for registering serial tissue sections (H&E and IHC) and a downstream analysis workflow utilizing foundation models to correlate morphological features with molecular data, including Response, Mismatch Repair (MMR) status, and Tumor Mutational Burden (TMB).
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