This actually looks like that: Proto-BagNets for local and global interpretability-by-design

• URL: http://arxiv.org/abs/2406.15168v2
• Date: Mon, 24 Jun 2024 08:13:07 GMT
• Title: This actually looks like that: Proto-BagNets for local and global interpretability-by-design
• Authors: Kerol Djoumessi, Bubacarr Bah, Laura Kühlewein, Philipp Berens, Lisa Koch,
• Abstract summary: Interpretability is a key requirement for the use of machine learning models in high-stakes applications. We introduce Proto-BagNets, an interpretable-by-design prototype-based model. Proto-BagNet provides faithful, accurate, and clinically meaningful local and global explanations.
• Score: 5.037593461859481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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