How King's College London used Encord to annotate videos 6.4x faster

Ulrik Stig Hansen
December 16, 2022
3 min read
Back to blogs
blog image

light-callout-cta Employing clinicians to label medical data is extremely expensive. Speed and accuracy are paramount. By leveraging Encord, the most senior (and thus, most expensive) clinician at KCL saw a 16x improvement in labeling efficiency, cutting model development time from 1 year to 2 months.
 

In a published study, “Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects,” researchers at King’s College London annotated endoscopy videos of polyps 6.4x faster on Encord’s platform. 

For researchers in the field, the accurate labeling of data remains “painstaking, cost-inefficient, [and] time-consuming.” Employing clinicians to label videos of pre-cancerous polyps is excessively expensive and thus inhibits the creation of large datasets.

The study compared Encord, an enterprise-grade solution, with CVAT, an open-source tool, to analyze the speed and accuracy of labeling on each platform. Using a sample of polyp videos from the Hyper-Kvasir dataset, annotators leveraged the functionality offered on each platform. 

null

On the Encord platform, annotators employed embedded intelligence features, including object tracking algorithms and functionality to train CNNs to annotate the data. After labeling a small number of frames, annotators built a micro-model that predicted the annotations for the remaining frames. With CVAT, the annotators drew bounding boxes & propagated them across frames using linear interpolation of box coordinates.

null

light-callout-cta “With the model assistance, we found a much higher increase in efficiency within Encord simply because most labels were produced by a trained model and did not require correction.”
 

The increased efficiency and accuracy decreases the time investment required by clinicians to annotate data and frees up their time for more productive activities. 

author-avatar-url
Written by Ulrik Stig Hansen
Ulrik is the President & Co-Founder of Encord. Ulrik started his career in the Emerging Markets team at J.P. Morgan. Ulrik holds an M.S. in Computer Science from Imperial College London. In his spare time, Ulrik enjoys writing ultra-low latency software applications in C++ and enjoys exper... see more
View more posts

Think Encord could be a good fit for your team as well?

Book a demo

Software To Help You Turn Your Data Into AI

Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.