5 citations · 6 across the 5 of their papers we have counts for
5 papers · 1 filter
Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation
Domen Preložnik, Žiga Špiclin
Inter-scanner variability of magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robus…
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov +77
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a sign…
Modality agnostic intracranial aneurysm detection through supervised vascular surface classification
Žiga Bizjak, Boštjan Likar, Franjo Pernuš +1
Intracranial aneurysms (IAs) are generally asymptomatic and thus often discovered incidentally on angiographic scans like 3D DSA, CTA and MRA. Skilled radiologists achieved a sensi…
Vascular surface segmentation for intracranial aneurysm isolation and quantification
Žiga Bizjak, Boštjan Likar, Franjo Pernuš +1
Predicting rupture risk and deciding on optimal treatment plan for intracranial aneurysms (IAs) is possible by quantification of their size and shape. For this purpose the IA has t…
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
Stefan Denner, Ashkan Khakzar, Moiz Sajid +4
Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cu…