4 papers
StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows
Youwan Mahé, Axel Plessis, Stéphanie Leplaideur +3
Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research environments due to, among o…
Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review
Youwan Mahé, Youwan Mahé, Elise Bannier +4
Unsupervised anomaly detection (UAD) based on deep generative modelling has been increasingly explored for identifying pathological brain abnormalities without requiring voxel-leve…
Unsupervised Detection of Post-Stroke Brain Abnormalities
Youwan Mahé, Elise Bannier, Stéphanie Leplaideur +2
Post-stroke MRI not only delineates focal lesions but also reveals secondary structural changes, such as atrophy and ventricular enlargement. These abnormalities, increasingly reco…
Stroke Lesion Segmentation in Clinical Workflows: A Modular, Lightweight, and Deployment-Ready Tool
Yann Kerverdo, Florent Leray, Youwan Mahé +2
Deep learning frameworks such as nnU-Net achieve state-of-the-art performance in brain lesion segmentation but remain difficult to deploy clinically due to heavy dependencies and m…