Recognizing Surgical Activities with Recurrent Neural Networks

Robert DiPietro, Colin Lea, Anand Malpani, Narges Ahmidi, S. Swaroop Vedula +3 more
6/20/2016
cs.CV

Official code: rdipietro/miccai-2016-surgical-activity-rec (44 stars, Apache-2.0, last commit 2019-01-14).

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 .

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Code Implementations(1)

rdipietro/miccai-2016-surgical-activity-recOfficial100%
Apache-2.0

Recognizing Surgical Activities with Recurrent Neural Networks

4414Shell, PythonMay 10, 20167 years agoApache-2.0

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Cite this paper

@article{dipietro2016recognizing,
  title  = {Recognizing Surgical Activities with Recurrent Neural Networks},
  author = {Robert DiPietro and Colin Lea and Anand Malpani and Narges Ahmidi and S. Swaroop Vedula and Gyusung I. Lee and Mija R. Lee and Gregory D. Hager},
  year   = {2016},
  eprint = {1606.06329},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1606.06329}
}

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