Michael D. Vasilakakis
Papers
1
Total Citations
15
H-Index
1
About
Michael D. Vasilakakis is a researcher in medical image analysis and computer vision, with a primary focus on advancing video capsule endoscopy (VCE) technology. His key contributions lie in developing weakly-supervised learning methods for automated lesion detection in gastrointestinal imaging, a critical area for non-invasive diagnostics. His most cited work, "Weakly-Supervised Lesion Detection in Video Capsule Endoscopy Based on a Bag-of-Colour Features Model" (2017, 15 citations), introduced an innovative approach that leverages colour feature representations to identify abnormalities without requiring exhaustive manual annotations—a significant step toward scalable, real-world clinical applications. This research addresses the challenge of limited labelled data in medical imaging, demonstrating how bag-of-features models can effectively detect lesions in the complex, frame-based environment of VCE. Vasilakakis’s work has been recognized for its practical impact, offering a pathway to more efficient and accessible gastrointestinal screening. His contributions continue to influence the integration of machine learning into endoscopic diagnostics, making him a notable figure in the intersection of computer vision and healthcare.
Research Focus
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Top Papers
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