167 citations · 1k across the 73 of their papers we have counts for
3 papers · 2 filters
Be Careful When Evaluating Explanations Regarding Ground Truth
Hubert Baniecki, Maciej Chrabaszcz, Andreas Holzinger +3
Evaluating explanations of image classifiers regarding ground truth, e.g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consid…
Towards Evaluating Explanations of Vision Transformers for Medical Imaging
Piotr Komorowski, Hubert Baniecki, Przemysław Biecek
As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many…
Interpretable machine learning for time-to-event prediction in medicine and healthcare
Hubert Baniecki, Bartlomiej Sobieski, Patryk Szatkowski +2
Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, onl…