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cs.CV2024
Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges
Shreyasi Pathak, Jörg Schlötterer, Jeroen Veltman +3
Deep learning models have achieved high performance in medical applications, however, their adoption in clinical practice is hindered due to their black-box nature. Self-explainabl…
cs.CV2023
Case-level Breast Cancer Prediction for Real Hospital Settings
Shreyasi Pathak, Jörg Schlötterer, Jeroen Geerdink +4
Breast cancer prediction models for mammography assume that annotations are available for individual images or regions of interest (ROIs), and that there is a fixed number of image…
cs.CV2023
Interpreting and Correcting Medical Image Classification with PIP-Net
Meike Nauta, Johannes H. Hegeman, Jeroen Geerdink +3
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…