A novel end-to-end multitask learning framework with a Spatio-Temporal and Channel Attention Module outperformed state-of-the-art models on surgical skill classification and score regression tasks.
A novel multitask learning framework improves automatic video-based surgical skill assessment over existing state-of-the-art models.
Surgical skill assessment (SSA) plays a vital role in medical systems for reducing intraoperative surgical errors and improving clinical outcomes. To ensure objective and efficient SSA, many automatic video-based SSA methods have been developed. In particular, various deep learning methods have been devised recently by utilising CNN or RNN-based networks for various skill assessment tasks (e.g., skill level prediction). While predicting overall skill levels and assessing detailed attribute-based scores are highly correlated, most existing studies deal with these two tasks separately, without fully exploiting different information sources encoded in a dataset. In contrast, we propose a novel end-to-end multitask learning framework to conduct skill level classification and attribute score regression jointly. Specifically, our network incorporates two branches for the two tasks, which share earlier layers for feature extraction and hold different prediction layers for specific targets. The shared feature extractor is optimised under the supervision of both tasks simultaneously, encouraging the model to consider information from different aspects and their relatedness to learn richer and more generalised features. In addition, since not every part of a surgical video contributes to skill assessment equally, we enhance an existing feature extractor I3D with a novel Spatio-Temporal & Channel Attention Module to emphasize important features. Experimental results on the public dataset JIGSAWS show that our proposed network outperforms state-of-the-art models on both skill classification and score regression tasks.
Jian et al. (Sun,) conducted a other in Surgical skill assessment. End-to-end multitask learning framework vs. State-of-the-art models was evaluated on Skill classification and score regression tasks. A novel end-to-end multitask learning framework with a Spatio-Temporal and Channel Attention Module outperformed state-of-the-art models on surgical skill classification and score regression tasks.