Case Studies
How King's College London used Encord to annotate videos 6.4x faster
Problem
Clinicians at King's College London (KCL) faced challenges in efficiently labeling medical data, particularly videos of pre-cancerous polyps in endoscopy, which was both time-consuming and costly. Employing clinicians for labeling was expensive and hindered the creation of large datasets necessary for AI model development.
Key Results
Clinicians at King's College London experienced significant improvements in labeling efficiency and reduced model development time by leveraging Encord's platform. With a 16x increase in efficiency for the most senior clinician and annotations completed 6.4x faster compared to using CVAT, Encord's embedded intelligence features and model assistance proved instrumental. This enhanced efficiency not only saved time but also reduced the cost associated with employing clinicians for data annotation, allowing them to focus on more productive tasks.
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.
Encord vs. CVAT
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.
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.
“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.
Yes. In addition to being able to train models & run inference using our platform, you can either import model predictions via our APIs & Python SDK, integrate your model in the Encord annotation interface if it is deployed via API, or upload your own model weights.
At Encord, we take our security commitments very seriously. When working with us and using our services, you can ensure your and your customer's data is safe and secure. You always own labels, data & models, and Encord never shares any of your data with any third party. Encord is hosted securely on the Google Cloud Platform (GCP). Encord native integrations with private cloud buckets, ensuring that data never has to leave your own storage facility.
Any data passing through the Encord platform is encrypted both in-transit using TLS and at rest.
Encord is HIPAA&GDPR compliant, and maintains SOC2 Type II certification. Learn more about data security at Encord here.Yes. If you believe you’ve discovered a bug in Encord’s security, please get in touch at security@encord.com. Our security team promptly investigates all reported issues. Learn more about data security at Encord here.
Yes - we offer managed on-demand premium labeling-as-a-service designed to meet your specific business objectives and offer our expert support to help you meet your goals. Our active learning platform and suite of tools are designed to automate the annotation process and maximise the ROI of each human input. The purpose of our software is to help you label less data.
The best way to spend less on labeling is using purpose-built annotation software, automation features, and active learning techniques. Encord's platform provides several automation techniques, including model-assisted labeling & auto-segmentation. High-complexity use cases have seen 60-80% reduction in labeling costs.
Encord offers three different support plans: standard, premium, and enterprise support. Note that custom service agreements and uptime SLAs require an enterprise support plan. Learn more about our support plans here.
Explore our products
Index
Manage & curate your data
Understand and manage your visual data, prioritize data for labeling, and initiate active learning pipelines.
Annotate
Supporting your labeling needs
Super charge your data annotation with AI-powered labeling — including automated interpolation, object detection and ML-based quality control.
Active
Find & fix data issues with ease
Monitor, troubleshoot, and evaluate the data and labels impacting model performance.
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.