5 research outputs found
Temporal coherence-based self-supervised learning for laparoscopic workflow analysis
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.Comment: Accepted at the Workshop on Context-Aware Operating Theaters (OR
2.0), a MICCAI satellite even