Temporal coherence-based self-supervised learning for laparoscopic workflow analysis

by   Isabel Funke, et al.

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, annotating surgical data requires expert knowledge, which is why collecting a sufficient amount of data is often difficult, 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 when compared to a non-pretrained neural network.


Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis

For many applications in the field of computer assisted surgery, such as...

Workflow Augmentation of Video Data for Event Recognition with Time-Sensitive Neural Networks

Supervised training of neural networks requires large, diverse and well ...

SurgMAE: Masked Autoencoders for Long Surgical Video Analysis

There has been a growing interest in using deep learning models for proc...

Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis

Data-driven approaches to assist operating room (OR) workflow analysis d...

"Train one, Classify one, Teach one" – Cross-surgery transfer learning for surgical step recognition

Prior work demonstrated the ability of machine learning to automatically...

Please sign up or login with your details

Forgot password? Click here to reset