Label-Guided Teacher for Surgical Phase Recognition via Knowledge Distillation

Automatic surgical phase recognition plays an essential role in developing advanced, context-aware, computer-assisted intervention systems. Knowledge distillation is an effective framework to transfer knowledge from a teacher network to a student network, which has been used to solve the challenging surgical phase recognition task. A key to a successful knowledge distillation is to learn a better teacher network. To this end, we propose a novel label-guided teacher network for knowledge distillation. Specifically, our teacher network takes both video frames and ground-truth labels as input. Instead of only using labels to supervise the final predictions, we additionally introduce two types of label guidance to learn a better teacher: 1) we propose label embedding-frame feature cross-attention transformer blocks for feature enhancement; and 2) we propose to use label information to sample positive (from same phase) and negative features (from different phases) in a supervised contrastive learning framework to learn better feature embeddings. Then, by minimizing feature similarity, the knowledge learnt by our teacher network is effectively distilled into a student network. At inference stage, the distilled student network can perform accurate surgical phase recognition taking only video frames as input. Comprehensive experiments are conducted on two laparoscopic cholecystectomy video datasets to validate the proposed method, offering an accuracy of 93.3 \(\,\pm \,\) 5.8% on the Cholec80 dataset and an accuracy of 91.6 \(\,\pm \,\) 9.1% on the M2cai16 dataset.