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Image Analysis in Stroke Diagnosis and Interventions

5th International Workshop, SWITCH 2025, Held in Conjunction with MICCAI 2025, Daejeon, South...

Ruisheng Su Ezequiel de la Rosa Linda Vorberg Jiong Zhang

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English
Springer Nature Switzerland AG
10 October 2025
This book constitutes the refereed proceedings of the 5th International Workshop on Stroke Imaging and Treatment , SWITCH 2025, held in conjunction with MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.

The 9 revised full papers presented in this volume were selected form 11 submissions. These papers highlight recent advances in image analysis for the diagnosis and intervention of ischemic and haemorrhagic stroke, with a focus on integrating artificial intelligence and computational techniques to address clinically relevant challenges. 
Edited by:   , , , ,
Imprint:   Springer Nature Switzerland AG
Country of Publication:   Switzerland
Dimensions:   Height: 235mm,  Width: 155mm, 
ISBN:   9783032079442
ISBN 10:   3032079446
Series:   Lecture Notes in Computer Science
Pages:   96
Publication Date:  
Audience:   College/higher education ,  Further / Higher Education
Format:   Paperback
Publisher's Status:   Active
Direct vascular territory segmentation on cerebral digital subtraction angiography.- Towards Diagnostic Quality Flat-Panel Detector CT Imaging Using Diffusion Models.- OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA.- CLAIRE-DSA: Fluoroscopic Image Classification for Quality Assurance of Computer Vision Pipelines in Acute Ischemic Stroke.- From Thresholds to Teachers: Correcting Unsupervised Learning for Arterial Calcifications in CTA.- Leveraging Last-Known-Well Times for Radiomics-based Stroke Onset Estimation from Non-Contrast CT.- Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI.- Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion stroke.- Self-Supervised Pretraining and Multi-Label Decoding for Intracranial Hemorrhage Segmentation.

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