Summary of Discovering Interpretable Models Of Scientific Image Data with Deep Learning, by Christopher J. Soelistyo and Alan R. Lowe
Discovering interpretable models of scientific image data with deep learning
by Christopher J. Soelistyo, Alan R. Lowe
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes methods for creating interpretable, domain-specific models from complex raw data like images. The goal is to use these models to derive scientific insights from the data. Specifically, the authors implement disentangled representation learning, sparse deep neural network training, and symbolic regression, and test their effectiveness in forming interpretable models of image data. Using a well-studied bioimaging dataset, they demonstrate that these methods can produce highly accurate, yet simple models that rival black-box benchmarks. The paper explores the utility of these interpretable models in providing scientific explanations of biological phenomena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to understand complex images and how this understanding can be used to gain scientific insights. Imagine having a special tool that helps you make sense of pictures, like recognizing cells in microscope images. This paper shows how to use new techniques to create simple models from these images, which can then help scientists explain what’s happening in the images. |
Keywords
* Artificial intelligence * Neural network * Regression * Representation learning
