Self-Knowledge Distillation for Surgical Phase Recognition

Purpose: Advances in surgical phase recognition are generally led by training deeper networks. Rather than going further with a more complex solution, we believe that current models can be exploited better. We propose a self-knowledge distillation framework that can be integrated into current state-of-the-art (SOTA) models without requiring any extra complexity to the models or annotations. Methods: Knowledge distillation is a framework for network regularization where knowledge is distilled from a teacher network to a student network. In self-knowledge distillation, the student model becomes the teacher such that the network learns from itself. Most phase recognition models follow an encoder-decoder framework. Our framework utilizes self-knowledge distillation in both stages. The teacher model guides the training process of the student model to extract enhanced feature representations from the encoder and build a more robust temporal decoder to tackle the over-segmentation problem. Results: We validate our proposed framework on the public dataset Cholec80. Our framework is embedded on top of four popular SOTA approaches and consistently improves their performance. Specifically, our best GRU model boosts performance by +3.33% accuracy and +3.95% F1-score over the same baseline model. Conclusion: We embed a self-knowledge distillation framework for the first time in the surgical phase recognition training pipeline. Experimental results demonstrate that our simple yet powerful framework can improve performance of existing phase recognition models. Moreover, our extensive experiments show that even with 75% of the training set we still achieve performance on par with the same baseline model trained on the full set.

CaDIS: Cataract Dataset for Image Segmentation

Video signals provide a wealth of information about surgical procedures and are the main sensory cue for surgeons. Video processing and understanding can be used to empower computer assisted interventions (CAI) as well as the development of detailed post-operative analysis of the surgical intervention. A fundamental building block to such capabilities is the ability to understand and segment video into semantic labels that differentiate and localize tissue types and different instruments. Deep learning has advanced semantic segmentation techniques dramatically in recent years but is fundamentally reliant on the availability of labelled datasets used to train models. In this paper, we introduce a high quality dataset for semantic segmentation in Cataract surgery. We generated this dataset from the CATARACTS challenge dataset, which is publicly available. To the best of our knowledge, this dataset has the highest quality annotation in surgical data to date. We introduce the dataset and then show the automatic segmentation performance of state-of-the-art models on that dataset as a benchmark.