Bayesian Uncertainty Estimation Improves nnU-Net Generalization to Unseen Sites for Stroke Lesion Segmentation
Vorberg L, Ditt H, Sühling M, Maier A, Murray N, Nicolaou S, Taubmann O (2025)
Publication Type: Conference contribution
Publication year: 2025
Journal
Publisher: Springer
Series: Lecture Notes in Computer Science
City/Town: Cham
Pages Range: 22-30
Conference Proceedings Title: Image Analysis in Stroke Diagnosis and Interventions
Event location: Marrakesh
ISBN: 9783031811005
DOI: 10.1007/978-3-031-81101-2_3
Abstract
This book constitutes the refereed proceedings of the 4th International MICCAI Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2024, as well as the Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, held in conjunction with MICCAI 2024, in Marrakesh, Morocco, on October 10, 2024. The 12 revised full papers presented in this volume were selected form 16 submissions. The papers describe research advancements in image analysis for the diagnosis and intervention of ischemic and haemorrhagic stroke and present the latest developments in segmentation, disease prognosis, stroke diagnosis and treatment, and other clinically relevant applications.
Authors with CRIS profile
Linda Vorberg
Lehrstuhl für Informatik 5 (Mustererkennung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Involved external institutions
Canada (CA)
Germany (DE)How to cite
APA:
Vorberg, L., Ditt, H., Sühling, M., Maier, A., Murray, N., Nicolaou, S., & Taubmann, O. (2025). Bayesian Uncertainty Estimation Improves nnU-Net Generalization to Unseen Sites for Stroke Lesion Segmentation. In Image Analysis in Stroke Diagnosis and Interventions (pp. 22-30). Marrakesh, MA: Cham: Springer.
MLA:
Vorberg, Linda, et al. "Bayesian Uncertainty Estimation Improves nnU-Net Generalization to Unseen Sites for Stroke Lesion Segmentation." Proceedings of the 4th International Workshop, SWITCH 2024, and 6th International Challenge, ISLES 2024, Marrakesh Cham: Springer, 2025. 22-30.
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