Interpretability analysis on a pathology foundation model reveals biologically relevant embeddings across modalities

Nhat Le, Ciyue Shen, Chintan Shah, Blake Martin, Daniel Shenker, Harshith Padigela, Jennifer A. Hipp, Sean Grullon, John Abel, Harsha Pokkalla, Dinkar Juyal

Abstract: Mechanistic interpretability has been explored in detail for large language models (LLMs). For the first time, we provide a preliminary investigation with similar interpretability methods for medical imaging. Specifically, we analyze the features from a ViT-Small encoder obtained from a pathology Foundation Model via application to two datasets: one dataset of pathology images, and one dataset of pathology images paired with spatial transcriptomics. We discover an interpretable representation of cell and tissue morphology, along with gene expression within the model embedding space. Our work paves the way for further exploration around interpretable feature dimensions and their utility for medical and clinical applications.