4 citations · 8 across the 4 of their papers we have counts for
4 papers
Feature importance to explain multimodal prediction models. A clinical use case
Jorn-Jan van de Beld, Shreyasi Pathak, Jeroen Geerdink +2
Surgery to treat elderly hip fracture patients may cause complications that can lead to early mortality. An early warning system for complications could provoke clinicians to monit…
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…
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…
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…