Robustness Against Out of Distribution Video Frames in Online Surgical Workflow Recognition with Temporal Convolutional Networks

Amirhossein Bayat, Kadir Kirtac, Salih karagoz, Julien Schwerin, Michael Stenzel, Marco Smit, Florian Aspart

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The automatic recognition of surgical phase based on laparoscopic videos is a pre-requisite to diverse AI application on surgeries. Online surgical phase recognition is commonly achieved using two-stages models combining (i) a spatial feature extraction at the frame level with a (ii) temporal model. Yet, this online surgical phase recognition is a challenging task in real-world scenarios. For example, the camera might be temporally extracted of the body during surgeries (e.g., to be cleaned). The Out-of-body (OOB) phases have out-of-distribution spatial features and have unpredictable occurrence which affect the temporal model performance. We propose a simple, yet effective, mechanism to robustify our temporal model against OOB phases. Our solution leverages the two-stages structure of surgical phase model predictions. We train and test our model on a large scale real-world dataset of laparoscopic cholecystectomy videos and show the effectiveness of our approach.
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Wednesday 6th July
Poster Session 1.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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