bibtext: @InProceedings{ Djo_This_MICCAI2024,
                   author = { Djoumessi, Kerol and Bah, Bubacarr and Kühlewein, Laura and Berens, Philipp and Koch, Lisa },
                   title = { { This actually looks like that: Proto-BagNets for local and global interpretability-by-design } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
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.
title: This actually looks like that: Proto-BagNets for local and global interpretability-by-design
authors: Djoumessi, Kerol Bah, Bubacarr Kühlewein, Laura Berens, Philipp Koch, Lisa