Bayesian Uncertainty Estimation Improves nnU-Net Generalization to Unseen Sites for Stroke Lesion Segmentation - Vorberg, Linda; Ditt, Hendrik; Sühling, Michael; Maier, Andreas; Murray, Nicolas; Nicolaou, Savvas; Taubmann, Oliver (Image Analysis in Stroke Diagnosis and Interventions, 2025)
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 application
Keywords: deep learning; medical image analysis; ischemic stroke; hemorrhagic stroke; endovascular thrombectomy; brain; segmentation; computer-aided diagnosis; machine learning; artificial intelligence; endovascular interventions; image-guided interventions; stroke