4 citations · 8 across the 5 of their papers we have counts for
5 papers
ConceptCF: Concept-based Counterfactuals for the Explainability of Time Series
Annemarie Jutte, Faizan Ahmed, Jeroen Linssen +1
This paper proposes ConceptCF, a method for counterfactual generation that operates on human-interpretable concepts. In high-stakes domains such as healthcare and predictive mainte…
Are Large Language Models the New Interface for Data Pipelines?
Sylvio Barbon Junior, Paolo Ceravolo, Sven Groppe +5
A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant at…
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…