Felix Bragman
Papers
1
Total Citations
19
H-Index
1
About
Felix Bragman is a researcher advancing the frontier of computer vision in medicine, with a primary focus on video semantic segmentation for surgical applications. His most cited work, "A spatio-temporal network for video semantic segmentation in surgical videos" (2023, 19 citations), introduces a novel deep learning architecture that captures both spatial and temporal dynamics in surgical footage, enabling more accurate, real-time identification of anatomical structures and surgical tools. This contribution is pivotal for developing intelligent surgical assistance systems, such as automated skill assessment and context-aware robotic guidance. Bragman’s research addresses the critical challenge of maintaining segmentation consistency across video frames—a key step toward safer, data-driven surgery. While his citation count reflects an emerging career, the technical depth and practical relevance of his work signal strong potential for future impact. By bridging spatio-temporal modeling with clinical video analysis, Bragman is helping to shape a new generation of AI tools that can interpret complex, dynamic surgical scenes, ultimately aiming to improve patient outcomes and surgical training.
Research Focus
Key Achievements
Top Papers
- 1A spatio-temporal network for video semantic segmentation in surgical videos19 citations · 2023