Co-occurrence balanced time series classification for the semi-supervised recognition of surgical smoke.
Journal:
International journal of computer assisted radiology and surgery
Published Date:
May 25, 2021
Abstract
PURPOSE: Automatic recognition and removal of smoke in surgical procedures can reduce risks to the patient by supporting the surgeon. Surgical smoke changes its visibility over time, impacting the vision depending on its amount and the volume of the body cavity. While modern deep learning algorithms for computer vision require large amounts of data, annotations for training are scarce. This paper investigates the use of unlabeled training data with a modern time-based deep learning algorithm.