Summary of This Actually Looks Like That: Proto-bagnets For Local and Global Interpretability-by-design, by Kerol Djoumessi et al.
This actually looks like that: Proto-BagNets for local and global interpretability-by-design
by Kerol Djoumessi, Bubacarr Bah, Laura Kühlewein, Philipp Berens, Lisa Koch
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 addresses the critical need for interpretable machine learning models in high-stakes applications like medical diagnosis. Current methods for explaining black-box models are often post-hoc and don’t accurately reflect the model’s behavior. Prototype-based networks have been proposed as a remedy, but they suffer from limitations such as providing coarse and unreliable explanations. The authors introduce Proto-BagNets, an interpretable-by-design prototype-based model that combines bag-of-local feature models and prototype learning to provide meaningful, coherent, and relevant prototypical parts for accurate image classification tasks. They evaluate the Proto-BagNet on publicly available retinal OCT data for drusen detection and find it performs comparably to state-of-the-art models while providing faithful and clinically meaningful local and global explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes machine learning models more understandable, which is important for using them in medical diagnosis. Right now, most models are “black boxes” that don’t explain how they work. The authors created a new type of model called Proto-BagNets that can provide clear reasons why it’s making certain decisions. They tested this model on retinal OCT data to detect drusen and found it works just as well as other good models, but also gives accurate explanations. |
Keywords
* Artificial intelligence * Image classification * Machine learning
