25 citations · 52 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans
Lisa Anita De Santi, Jörg Schlötterer, Michael Scheschenja +4
Information from neuroimaging examinations is increasingly used to support diagnoses of dementia, e.g., Alzheimer's disease. While current clinical practice is mainly based on visu…
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…