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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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  1. ConvLSTM-Surgical-Tool-Tracker ConvLSTM-Surgical-Tool-Tracker Public

    This repo contains an implementation code for the weakly supervised surgical tool tracker. In this research, the temporal dependency in surgical video data is modeled using a convolutional LSTM whi…

    Python 41 12

  2. TF-Cholec80 TF-Cholec80 Public

    Library packaging the Cholec80 dataset for easy handling with Tensorflow.

    Jupyter Notebook 38 6

  3. ORPose-Depth ORPose-Depth Public

    Inference demo and evaluation scripts for the MICCAI-2019 paper "Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images"

    Python 22 4

  4. SelfSupSurg SelfSupSurg Public

    Official repository for "Dissecting Self-Supervised Learning Methods for Surgical Computer Vision"

    Python 39 12

  5. MVOR MVOR Public

    Multi-View Operating Room (MVOR) dataset consists of synchronized multi-view frames recorded by three RGB-D cameras in a hybrid OR during real clinical interventions. We provide camera calibration …

    Python 60 9

  6. cholect50 cholect50 Public

    A repository for surgical action triplet dataset. Data are videos of laparoscopic cholecystectomy that have been annotated with <instrument, verb, target> labels for every surgical fine-grained act…

    Python 55 5

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