Pathology is the study of microscopic inspection of tissue. Machine learning (ML) has been applied to pathology images for various tasks.Interpretability is crucial for ML in medical imaging for building decision-makers’ trust, debugging silent failure modes and shortcut-learning, and reducing the risks of catastrophic model failures in real-world deployments. Work on interpretability in pathology has focused on assigning spatial credit to Whole slide image (WSI)-level predictions, computing human-interpretable features from model output heatmaps and visualization of multi-head self-attention values on image patches. Mechanistic interpretability has been explored in detail for large language models (LLMs)1 but remained underexplored for vision models, especially in the field of pathology.
Conference
ICML 2024
Partner
Gilead Science
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