Vision

The goal was to democratize access to personalized medicine by proving it is feasible to predict genomic biomarkers directly from standard, low-cost pathology images.

Problem Statement

Personalized medicine relies on knowing a patient's genomic status (e.g., Microsatellite Instable (MSI) vs Microsatellite Stable (MSS)), but genomic testing is expensive, slow, and often inaccessible. However, standard H&E stained biopsies are routinely taken for almost every cancer patient.

If we could predict genomic status from these standard images, we could significantly reduce the cost and time required to determine the correct treatment plan, effectively screening every patient for eligibility for advanced immunotherapies.

Methodology

I worked to recreate and industrialize the results of a prominent Nature Medicine paper (Kather et al.) which proposed using Deep Learning for this task.

• Multiple Instance Learning (MIL): Implemented a weakly-supervised deep learning approach because slide labels are known (MSI/MSS) but the specific regions of the tumor causing that status are not.
• Geospatial Processing: Used Shapely and GeoPandas to handle the geometry of tissue regions and tile management at a massive scale.
• MLOps: Established best practices for reproducibility using DVC (Data Version Control) to track experiments, data lineage, and model versions.

Outcomes

• Identified Critical Flaws: During reproduction, I discovered foundational mistakes in the original paper's methodology concerning data leakage across slide tiles. Fixing this led to more realistic and robust performance estimates.
• Commercial Success: The successful proof-of-concept and rigorous validation unlocked a £2M grant for Sonrai Analytics to further develop the technology.
• Feasibility Proven: Demonstrated that high-accuracy classification is possible using only standard H&E images, without requiring expensive immunofluorescence staining.