Interpretability analysis on a pathology foundation model reveals biologically relevant embeddings across modalities
<|reference_start|>Interpretability analysis on a pathology foundation model reveals biologically relevant embeddings across modalities: 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.<|reference_end|>
@article{le2024interpretability,
    title={Interpretability analysis on a pathology foundation model reveals
  biologically relevant embeddings across modalities},
    author={Nhat Le, Ciyue Shen, Chintan Shah, Blake Martin, Daniel Shenker,
  Harshith Padigela, Jennifer Hipp, Sean Grullon, John Abel, Harsha Vardhan
  Pokkalla, Dinkar Juyal},
    journal={arXiv preprint arXiv:2407.10785},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10785},
    primaryClass={eess.IV cs.CV}
}