Papers › Temporal coherence-based self-supervised learning for laparoscopic workflow analysis
Temporal coherence-based self-supervised learning for laparoscopic workflow analysis
Isabel Funke, Alexander Jenke, Sören Torge Mees, Jürgen Weitz, Stefanie Speidel, Sebastian Bodenstedt
In order to provide the right type of assistance at the right time, computer-assisted surgery systems need context awareness. To achieve this, methods for surgical workflow analysis are crucial. Currently, convolutional neural networks provide the best performance for video-based workflow analysis tasks. For training such networks, large amounts of annotated data are necessary. However, collecting a sufficient amount of data is often costly, time-consuming, and not always feasible. In this paper, we address this problem by presenting and comparing different approaches for self-supervised pretraining of neural networks on unlabeled laparoscopic videos using temporal coherence. We evaluate our pretrained networks on Cholec80, a publicly available dataset for surgical phase segmentation, on which a maximum F1 score of 84.6 was reached. Furthermore, we were able to achieve an increase of the F1 score of up to 10 points when compared to a non-pretrained neural network.
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