Summary of Towards Scientific Discovery with Dictionary Learning: Extracting Biological Concepts From Microscopy Foundation Models, by Konstantin Donhauser et al.
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
by Konstantin Donhauser, Kristina Ulicna, Gemma Elyse Moran, Aditya Ravuri, Kian Kenyon-Dean, Cian Eastwood, Jason Hartford
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores whether dictionary learning (DL) can be used to discover unknown concepts from less human-interpretable scientific data, such as cell images. The authors study microscopy foundation models trained on multi-cell image data and show that sparse dictionaries extract biologically-meaningful concepts like cell type and genetic perturbation type. They also propose Iterative Codebook Feature Learning (ICFL) and demonstrate its effectiveness in improving feature selectivity compared to TopK sparse autoencoders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a powerful tool called dictionary learning to understand how large language models work. It’s like trying to figure out what words are hidden inside a big book. The researchers tested this method on special images of cells and found that it could identify important things about the cells, like what kind they are or what’s happening to them. They also developed a new way to use dictionary learning that makes it even better at finding important information. |
