AI Optimism
Surgical World Models in Robotic Surgery: A Review
M Boels, H Robertshaw, T C Booth, A Granados, P Dasgupta and S Ourselin
Submitted to IEEE T-MRB
I'm Maxence, a 4th year PhD student at King’s College London, working with the SIE-CAI Lab since 2021 under the guidance of Prof. Sebastien Ourselin. Prior to my doctoral studies, I earned an MSc in Machine Learning, Computer Vision, and Robotics from the University of Surrey, where I conducted research at the CVSSP Lab. My goal is to help develop superintelligent and autonomous systems for a better future.
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AI Research Terminal
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Research Interests
- Surgical Video Understanding: Recognising surgical anatomical structures and procedural steps—instruments, organs, gestures, and phases— from long and complex surgical videos using deep learning models [ LoViT ],
- Expert Imitation Learning: Generating and mimicking expert-level surgical workflows by learning from expert demonstrations using video recordings for planning and decision-making in surgical procedures [ SuPRA | SWAG ],
- Workflow Discovery and Planning: Selecting optimal next surgical actions through reasoning and planning about expected future rewards using reinforcement learning, and discovering optimal surgical workflows that maximise desired procedural outcomes for patients, searching for new pathways beyond expert demonstrations [ DARIL ].
Featured Projects
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ROBOTICS - DEFENSE
Tracking Turret
Pan-Tilt Laser Defense Turret
ROBOTICS - DRONE
FPV Drone
Building and flying a custom FPV drone from scratch
AERIAL INTELLIGENCE
Trajectory Planning
European Tech Defense Hackathon - Tactical Anticipation
COMPUTER VISION
Aerial Intelligence
Drone Navigation Platform
APPLICATION
Surgical Assistant
Video Understanding and LLM Guidance
Short Biography
Maxence's PhD research focuses on surgical video understanding, expert imitation learning, and workflow discovery and planning. He is also interested in the intersection of AI and robotics, particularly in the context of autonomous drones and aerial intelligence. Maxence has a strong background in machine learning, computer vision, and robotics, with a focus on developing innovative solutions for real-world challenges. He has published several papers in top-tier conferences and journals, including MICCAI and IJCARS. His work is currently supported by UKRI funding.