This actually looks like that: Proto-BagNets for local and global interpretability-by-design
<|reference_start|>This actually looks like that: Proto-BagNets for local and global interpretability-by-design: 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.<|reference_end|>
@article{djoumessi2024this,
    title={This actually looks like that: Proto-BagNets for local and global
  interpretability-by-design},
    author={Kerol Djoumessi, Bubacarr Bah, Laura K"uhlewein, Philipp Berens, Lisa
  Koch},
    journal={arXiv preprint arXiv:2406.15168},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15168},
    primaryClass={cs.AI}
}