Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects
Ulrik Stig Hansen, Eric Landau, Mehul Patel, Bu Hayee · medRxiv · 2020
Abstract The contribution of artificial intelligence (AI) to endoscopy is rapidly expanding. Accurate labelling of source data (video frames) remains the rate-limiting step for such projects and is a painstaking, cost-inefficient, time-consuming process. A novel software platform, Cord Vision (CdV) allows automated annotation based on ‘embedded intelligence’. The user manually labels a representative proportion of frames in a section of video (typically 5%), to create ‘micro-models’ which allow accurate propagation of the label throughout the remaining video frames. This could drastically reduce the time required for annotation. We conducted a comparative study with an open-source labelling platform (CVAT) to determine speed and accuracy of labelling. Across 5 users, CdV resulted in a significant increase in labelling performance (p 97% accuracy for bounding box placement. This advance represents a valuable first step in Al-image analysis projects.