Recognizing Surgical Activities with Recurrent Neural Networks

Code

• github.com/rdipietro/miccai-2016-surgical-activity-rec
  OfficialIn papertf★ 0
• github.com/Skori/miccai-2016-surgical-activity-rec-Refactored
  tf★ 0
• github.com/krishnanpooja/Master-Thesis
  tf★ 0

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 .

Tasks

Benchmark Results

Reproductions