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- W4385017860 abstract "Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we evaluate its accuracy and interpretability for fracture detection and skin cancer diagnosis. We find that PIP-Net's decision making process is in line with medical classification standards, while only provided with image-level class labels. Because of PIP-Net's unsupervised pretraining of prototypes, data quality problems such as undesired text in an X-ray or labelling errors can be easily identified. Additionally, we are the first to show that humans can manually correct the reasoning of PIP-Net by directly disabling undesired prototypes. We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging." @default.
- W4385017860 created "2023-07-22" @default.
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- W4385017860 date "2023-07-19" @default.
- W4385017860 modified "2023-09-23" @default.
- W4385017860 title "Interpreting and Correcting Medical Image Classification with PIP-Net" @default.
- W4385017860 doi "https://doi.org/10.48550/arxiv.2307.10404" @default.
- W4385017860 hasPublicationYear "2023" @default.
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