7 papers
Universal Algorithm-Implicit Learning
Stefano Woerner, Seong Joon Oh, Christian F. Baumgartner
Current meta-learning methods are constrained to narrow task distributions with fixed feature and label spaces, limiting applicability. Moreover, the current meta-learning literatu…
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset
Stefano Woerner, Arthur Jaques, Christian F. Baumgartner
While the field of medical image analysis has undergone a transformative shift with the integration of machine learning techniques, the main challenge of these techniques is often…
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
Susu Sun, Stefano Woerner, Andreas Maier +2
Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions…
Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification
Susu Sun, Dominique van Midden, Geert Litjens +1
Multiple Instance Learning (MIL) methods have succeeded remarkably in histopathology whole slide image (WSI) analysis. However, most MIL models only offer attention-based explanati…
Subgroup Performance Analysis in Hidden Stratifications
Alceu Bissoto, Trung-Dung Hoang, Tim Flühmann +3
Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level…
Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology
Susu Sun, Leslie Tessier, Frédérique Meeuwsen +4
Multiple Instance Learning (MIL) methods allow for gigapixel Whole-Slide Image (WSI) analysis with only slide-level annotations. Interpretability is crucial for safely deploying su…