Abstract
Automatic activity detection is an important component for developing technologies that enable next generation surgical devices and workflow monitoring systems. In many application, the videos of interest are long and include several activities; hence, the deep models designed for such purposes consist of a backbone and a temporal sequence modeling architecture. In this paper, we investigate both the state-of-the-art activity recognition and temporal models to find the architectures that yield the highest performance. We first benchmark these models on a large-scale activity recognition dataset in the operating room with over 800 full-length surgical videos. However, since most other medical applications lack such a large dataset, we further evaluate our models on the Cholec80 surgical phase segmentation dataset, consisting of only 40 training videos. For backbone architectures, we investigate both 3D ConvNets and most recent transformer-based models; for temporal modeling, we include temporal ConvNets, RNNs, and transformer models for a comprehensive and thorough study. We show that even in the case of limited labeled data, we can outperform the existing work by benefiting from models pre-trained on other tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2205.02805
