Recognizing Surgical Activities with Recurrent Neural Networks

June 20, 2016 · Entered Twilight · 🏛 International Conference on Medical Image Computing and Computer-Assisted Intervention

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Repo contents: .gitignore, LICENSE, README.md, data.py, metrics.py, models.py, optimizers.py, scripts, standardize_jigsaws.py, train_and_summarize.py

Authors Robert DiPietro, Colin Lea, Anand Malpani, Narges Ahmidi, S. Swaroop Vedula, Gyusung I. Lee, Mija R. Lee, Gregory D. Hager arXiv ID 1606.06329 Category cs.CV: Computer Vision Citations 132 Venue International Conference on Medical Image Computing and Computer-Assisted Intervention Repository https://github.com/rdipietro/miccai-2016-surgical-activity-rec ⭐ 45 Last Checked 1 month ago

Abstract

We apply recurrent neural networks to the task of recognizing surgical activities from robot kinematics. Prior work in this area focuses on recognizing short, low-level activities, or gestures, and has been based on variants of hidden Markov models and conditional random fields. In contrast, we work on recognizing both gestures and longer, higher-level activites, or maneuvers, and we model the mapping from kinematics to gestures/maneuvers with recurrent neural networks. To our knowledge, we are the first to apply recurrent neural networks to this task. Using a single model and a single set of hyperparameters, we match state-of-the-art performance for gesture recognition and advance state-of-the-art performance for maneuver recognition, in terms of both accuracy and edit distance. Code is available at https://github.com/rdipietro/miccai-2016-surgical-activity-rec .