Optimization and Ensemble Techniques of Classification Models for Action Segmentation in Laparoscopic Surgery Videos

Abstract

This study investigates effective action segmentation to identify the surgeon’s technique from surgical videos for use in the automatic evaluation of surgical skill in laparoscopic surgery. We adopt the MS-TCN and its enhanced version, MS-TCN++, as action classification models and explore the optimal number of prediction layers and refinement layers for both models. Evaluation experiments after optimization show that both models have nearly equivalent classification accuracies but exhibit different prediction tendencies. Based on this observation, we propose ensemble methods that integrates the classification results of both models. We demonstrate that by using a method based on the sum of the prediction probabilities of both models, the accuracy improves.