Learning biologically relevant features in a pathology foundation model using sparse autoencoders

Understand

Pathology plays an important role in disease diagnosis, treatment decision-making and drug development.

• Previous works on interpretability for machine learning models on pathology images have revolved around methods such as attention value visualization and deriving human-interpretable features from model heatmaps.
• Mechanistic interpretability is an emerging area of model interpretability that focuses on reverse-engineering neural networks.
• Sparse Autoencoders (SAEs) have emerged as a promising direction in terms of extracting monosemantic features from polysemantic model activations.