Robustness Against Out of Distribution Video Frames in Online Surgical Workflow Recognition with Temporal Convolutional Networks
Keywords: Temporal Convolutional Networks, Surgical Workflow, OOD Detection
TL;DR: Robustness Against Out of Distribution Video Frames in Online Surgical Workflow Recognition with Temporal Convolutional Networks
Abstract: 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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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Application: Endoscopy
Secondary Subject Area: Detection and Diagnosis
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