The research group CAMMA (Computational Analysis and Modeling of Medical Activities) led by Prof. Nicolas Padoy aims at developing new tools and methods based on computer vision, medical image analysis and machine learning to perceive, model, analyze and support clinician and staff activities in the operating room (OR) using the vast amount of digital data generated during surgeries. We are a joint group of the University of Strasbourg and the IHU MixSurg institute. We are also part of the wider research team AVR (Automatics, Vision and Robotics) in the ICube institute. We are located on the campus of Strasbourg’s University Hospital in the facilities of IHU Strasbourg and collaborate closely with the IRCAD institute and the Nouvel Hopital Civil.
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- SelfPose3d Public
Official code for "SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation"
CAMMA-public/SelfPose3d’s past year of commit activity - Endoscapes Public
Official Repository for the Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment
CAMMA-public/Endoscapes’s past year of commit activity - surgitrack Public
CAMMA-public/surgitrack’s past year of commit activity - peskavlp.github.io Public
CAMMA-public/peskavlp.github.io’s past year of commit activity - ivtmetrics Public template
A Python evaluation metrics package for surgical action triplet recognition
CAMMA-public/ivtmetrics’s past year of commit activity