Recognizing Surgical Activities with Recurrent Neural Networks

Autor:	DiPietro, Robert, Lea, Colin, Malpani, Anand, Ahmidi, Narges, Vedula, S. Swaroop, Lee, Gyusung I., Lee, Mija R., Hager, Gregory D.
Rok vydání:	2016
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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 . Comment: Conditionally accepted at MICCAI 2016
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1606.06329 Zobrazit plný text záznamu View this record from Arxiv