Recognizing Surgical Activities with Recurrent Neural Networks
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 .
Cite
@article{arxiv.1606.06329,
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},
journal= {arXiv preprint arXiv:1606.06329},
year = {2016}
}
Comments
Conditionally accepted at MICCAI 2016