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We’re excited to launch the second AFRICAI Journal Club – in the series where we come together to critically engage with cutting-edge scientific papers in the field of AI and healthcare. On June 4th, we are pleased to welcome Ajibola Samson Oladokun, who will present the following paper:
🧾 Yi Shi et al. (2024), CS3: Cascade SAM for Sperm Segmentation. https://doi.org/10.1007/978-3-031-72384-1_56
Ajibola Samson Oladokun is a final year PhD candidate in Biomedical Engineering at the University of Cape Town, South Africa. He holds an MSc in Microprocessor and Control Engineering from the University of Ibadan, Nigeria, and a B.Eng. in Electronics and Electrical Engineering from Osun State University, Nigeria. He was recognized as an Outstanding Reviewer for MICCAI 2024 and his paper “SpeChrOmics” was accepted for oral presentation at the conference. He currently works as a research and development engineer at IMT Atlantique.
Why this paper?
Medical image segmentation is one of the most important tasks in medical image analysis. It involves the identification and delineation of specific structures or regions of interest, such as organs, tissues, or lesions, for the purpose of anomaly detection or disease diagnosis. Many medical imaging applications face the challenge of a limited labelled dataset, which constrains the effectiveness of segmentation when utilizing state-of-the-art deep learning models. This article proposes CS3, an unsupervised model that can be used to perform segmentation on unlabeled medical images. The model is based on the Segment Anything Model (SAM) by Meta, which has been trained on 11 million natural images. In this article, CS3 is applied for the complex segmentation of intertwined sperm cells in microscopic images. Thus, the model has the potential to facilitate unsupervised segmentation in other medical applications.
Event Details
• Date: Wednesday, June 4th, 2025
• Time: 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
Shi, Y. et al. (2024). CS3: Cascade SAM for Sperm Segmentation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15003. Springer, Cham. https://doi.org/10.1007/978-3-031-72384-1_56
