This actually looks like that: Proto-BagNets for local and global interpretability-by-design
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909229946765312 |
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| author | Djoumessi, Kerol Bah, Bubacarr Kühlewein, Laura Berens, Philipp Koch, Lisa |
| author_facet | Djoumessi, Kerol Bah, Bubacarr Kühlewein, Laura Berens, Philipp Koch, Lisa |
| contents | Interpretability is a key requirement for the use of machine learning models in high-stakes applications, including medical diagnosis. Explaining black-box models mostly relies on post-hoc methods that do not faithfully reflect the model's behavior. As a remedy, prototype-based networks have been proposed, but their interpretability is limited as they have been shown to provide coarse, unreliable, and imprecise explanations. In this work, we introduce Proto-BagNets, an interpretable-by-design prototype-based model that combines the advantages of bag-of-local feature models and prototype learning to provide meaningful, coherent, and relevant prototypical parts needed for accurate and interpretable image classification tasks. We evaluated the Proto-BagNet for drusen detection on publicly available retinal OCT data. The Proto-BagNet performed comparably to the state-of-the-art interpretable and non-interpretable models while providing faithful, accurate, and clinically meaningful local and global explanations. The code is available at https://github.com/kdjoumessi/Proto-BagNets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15168 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | This actually looks like that: Proto-BagNets for local and global interpretability-by-design Djoumessi, Kerol Bah, Bubacarr Kühlewein, Laura Berens, Philipp Koch, Lisa Artificial Intelligence Interpretability is a key requirement for the use of machine learning models in high-stakes applications, including medical diagnosis. Explaining black-box models mostly relies on post-hoc methods that do not faithfully reflect the model's behavior. As a remedy, prototype-based networks have been proposed, but their interpretability is limited as they have been shown to provide coarse, unreliable, and imprecise explanations. In this work, we introduce Proto-BagNets, an interpretable-by-design prototype-based model that combines the advantages of bag-of-local feature models and prototype learning to provide meaningful, coherent, and relevant prototypical parts needed for accurate and interpretable image classification tasks. We evaluated the Proto-BagNet for drusen detection on publicly available retinal OCT data. The Proto-BagNet performed comparably to the state-of-the-art interpretable and non-interpretable models while providing faithful, accurate, and clinically meaningful local and global explanations. The code is available at https://github.com/kdjoumessi/Proto-BagNets. |
| title | This actually looks like that: Proto-BagNets for local and global interpretability-by-design |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.15168 |