Self-supervised representation learning for surgical activity recognition.

ABSTRACT:

Purpose

Virtual reality-based simulators have the potential to become an essential part of surgical education. To make full use of this potential, they must be able to automatically recognize activities performed by users and assess those. Since annotations of trajectories by human experts are expensive, there is a need for methods that can learn to recognize surgical activities in a data-efficient way.

Methods

We use self-supervised training of deep encoder-decoder architectures to learn representations of surgical trajectories from video data. These representations allow for semi-automatic extraction of features that capture information about semantically important events in the trajectories. Such features are processed as inputs of an unsupervised surgical activity recogn

SUBMITTER: Paysan D 

PROVIDER: S-EPMC8589823 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature