Recognizing Surgical Activities with Recurrent Neural Networks
| Autor: | S. Swaroop Vedula, Gregory D. Hager, Robert S. DiPietro, Mija R. Lee, Anand Malpani, Gyusung Lee, Colin Lea, Narges Ahmidi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Conditional random field
Robot kinematics Computer science Speech recognition 0206 medical engineering 02 engineering and technology 020601 biomedical engineering 030218 nuclear medicine & medical imaging 03 medical and health sciences Task (computing) 0302 clinical medicine Recurrent neural network Gesture recognition Edit distance Hidden Markov model Gesture |
| Zdroj: | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 ISBN: 9783319467191 MICCAI (1) |
| DOI: | 10.1007/978-3-319-46720-7_64 |
| 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|