5 papers
A Critical Perspective on Finite Sample Conformal Prediction Theory in Medical Applications
Klaus-Rudolf Kladny, Bernhard Schölkopf, Lisa Koch +2
Machine learning (ML) is transforming healthcare, but safe clinical decisions demand reliable uncertainty estimates that standard ML models fail to provide. Conformal prediction (C…
Is Uncertainty Quantification a Viable Alternative to Learned Deferral?
Anna M. Wundram, Christian F. Baumgartner
Artificial Intelligence (AI) holds the potential to dramatically improve patient care. However, it is not infallible, necessitating human-AI-collaboration to ensure safe implementa…
Conformal Performance Range Prediction for Segmentation Output Quality Control
Anna M. Wundram, Paul Fischer, Michael Muehlebach +2
Recent works have introduced methods to estimate segmentation performance without ground truth, relying solely on neural network softmax outputs. These techniques hold potential fo…
PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration
Leonard Siegert, Paul Fischer, Mattias P. Heinrich +1
Deformable image registration is fundamental to many medical imaging applications. Registration is an inherently ambiguous task often admitting many viable solutions. While neural…
Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy
Paul Fischer, Hannah Willms, Moritz Schneider +3
Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during…