Papers
10
Total Citations
409
H-Index
7
About
Raphael Sznitman is a leading figure in computer-assisted surgery, specializing in surgical vision, robotic retinal microsurgery, and machine learning for medical interventions. His work has fundamentally advanced how surgical instruments are detected, segmented, and tracked in minimally invasive procedures, particularly in ophthalmology. His most influential contribution, the 2018 paper on articulated multi-instrument 2-D pose estimation using fully convolutional networks (136 citations), pioneered deep learning approaches for understanding complex instrument configurations in surgical videos. Sznitman has also made seminal contributions to retinal surgery, developing data-driven visual tracking methods (72 citations) and robotic systems for vitreoretinal procedures (53 citations). His research on instrument segmentation and tracking in minimally invasive surgery (50 citations) has become a benchmark in the field. More recently, he has explored force classification through simulation-trained neural networks and safety-critical applications like out-of-distribution detection for robotically guided microsurgery. His work on the GEYEDANCE platform, integrating Optical Coherence Tomography with robotic feedback, represents the cutting edge of multi-modal surgical assistance. With over 400 total citations, Sznitman continues to shape the future of computer-assisted ophthalmic surgery.
Research Focus
Key Achievements
Top Papers
- 1
- 2Data-Driven Visual Tracking in Retinal Microsurgery72 citations · 2012
- 3Robotic Retinal Surgery53 citations · 2019
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- 5Visual Tracking of Surgical Tools for Proximity Detection in Retinal Surgery42 citations · 2011
- 6Mask then classify: multi-instance segmentation for surgical instruments24 citations · 2021
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