3 papers
cs.CV2026
Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation
Tasneem Nasser, Susanne Schmid, Roberto Souza +1
A key challenge in medical image analysis is the scarcity of large annotated datasets for specific populations and diseases. As deep learning models rely heavily on labeled data, e…
cs.CV2026
Beyond Performance Metrics: Uncertainty Mapping of Label Ambiguity in Fazekas Score Prediction
Susanne Schmid, Johanna Ospel, Richard Frayne +1
Reference labels used to train medical image classification models are not always as certain as they may appear, and this uncertainty has implications on performance metrics. In th…
cs.CV2026
Contrast-Informed Augmentation and Domain-Adversarial Training for Adult-to-Neonatal MR Reconstruction Generalization
Stephen Moore, Lara Leijser, Richard Frayne +1
Purpose: To investigate whether contrast-informed data augmentation and domain-adversarial training improve the adult-to-neonatal generalization of the E2E-VarNet. Methods: Three t…