Marcel Breeuwer
Papers
3
Total Citations
6
H-Index
2
About
Marcel Breeuwer is a leading researcher at the intersection of artificial intelligence and robotic surgery, with a primary focus on robot-assisted minimally invasive esophagectomy (RAMIE) for esophageal cancer. His major contributions center on developing and benchmarking deep learning models for surgical phase recognition and real-time anatomical segmentation. Breeuwer’s work is distinguished by its clinical rigor: he has critically evaluated whether standard segmentation metrics truly reflect clinical reality, advocating for surgeon-centered evaluation frameworks. His 2025 benchmarking studies—cited 3 and 2 times respectively—demonstrate how pretrained attention-based models can enhance real-time recognition in RAMIE, a procedure known for its complexity across multiple anatomical areas. Notably, Breeuwer’s research bridges the gap between technical AI performance and practical surgical utility, ensuring that automated tools meet the nuanced needs of operating surgeons. His work is pivotal for advancing computer-assisted interventions in high-stakes oncological surgery, making him a key figure in the push toward safer, more efficient robotic procedures.
Research Focus
Key Achievements
Top Papers
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