We’re excited to launch the fourth edition of the AFRICAI Journal Club who is presented by Toufiq Musah– part of our ongoing series that brings together researchers, students, and professionals to critically engage with cutting-edge scientific papers at the intersection of AI and healthcare.
🧾 Zhang, D., Confidence, R., Anazodo, U. (2022). “Stroke Lesion Segmentation from Low-Quality and Few-Shot MRIs via Similarity-Weighted Self-ensembling Framework.”
DOI: https://doi.org/10.1007/978-3-031-16443-9_9
Why this paper?
It presents a deep learning framework that adapts models trained on glioma segmentation to the task of stroke lesion segmentation in a few-shot setting. This is important because of the rising burden of stroke in low- and middle-income countries, where access to large, high-quality datasets is limited. By making use of publicly available glioma data and introducing ways to handle low-resolution and limited stroke data, the method offers a practical path toward improving diagnostic capabilities in resource-constrained settings.
Toufiq Musah is a Biomedical Research Assistant & Engineer at the Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR), Ghana. His work involves applying artificial intelligence and deep learning methods to clinical decision support systems, and medical image analysis. Toufiq’s research interests include neuroimaging, medical image computing, and the development of practical clinical tools aimed at improving healthcare access in resource-limited environments. Some of his favourite pastime activities outside of work include participating in hackathons, teaching, drawing, and making video games.
Event Details
• Date: Tuesday, August 5, 2025
• Time: 13:00 GMT | 14:00 WAT | 15:00 CAT | 15:00 CEST
• Duration: 60 minutes (30-minute talk + 30-minute discussion)
• Location: Online via Zoom (link shared upon registration)
• Register now: this link.
Full article Reference
Zhang, D., Confidence, R., Anazodo, U. (2022). Stroke Lesion Segmentation from Low-Quality and Few-Shot MRIs via Similarity-Weighted Self-ensembling Framework. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham. https://doi.org/10.1007/978-3-031-16443-9_9
