Surgical Phase Recognition in Laparoscopic Cholecystectomy

Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis. Previous works on automated recognition of surgical phases utilized popular methods in computer vision but failed to consider the sequential nature of surgical procedures. In this paper, we propose a method that utilizes calibrated confidence scores to dynamically switch between two Transformer-based models, viz., baseline and a separately trained binary classifier model, depending on the calibrated confidence level. Our method outperforms the baseline model on the publicly available Cholec80 dataset and can be readily applied to a variety of phase recognition methods and applications.