Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models

Dictionary learning (DL) has emerged as a powerful interpretability tool for large language models. By extracting known concepts (e.g., Golden-Gate Bridge) from human-interpretable data (e.g., text), sparse DL can elucidate a model's inner workings. In this work, we ask if DL can also be used to discover unknown concepts from less human-interpretable scientific data (e.g., cell images), ultimately enabling modern approaches to scientific discovery. As a first step, we use DL algorithms to study microscopy foundation models trained on multi-cell image data, where little prior knowledge exists regarding which high-level concepts should arise. We show that sparse dictionaries indeed extract biologically-meaningful concepts such as cell type and genetic perturbation type. We also propose Iterative Codebook Feature Learning~(ICFL) and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity of extracted features compared to TopK sparse autoencoders.

@article{donhauser2025_2412.16247,
  title={ Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models },
  author={ Konstantin Donhauser and Kristina Ulicna and Gemma Elyse Moran and Aditya Ravuri and Kian Kenyon-Dean and Cian Eastwood and Jason Hartford },
  journal={arXiv preprint arXiv:2412.16247},
  year={ 2025 }
}