Medical Image Annotation for AI Development
.png)
Client
A startup developing computer vision solutions for medical image analysis, including X-rays, CT scans, and MRIs.
Client Goals
Train an AI model to detect pathologies
Create a benchmark dataset for internal testing
Ensure ultra-high annotation accuracy
Company Profile
The client is a leading AI-driven healthcare company specializing in medical imaging solutions. Their technology assists radiologists by providing AI-powered diagnostics based on annotated medical scans.
Project Overview
A medical AI company needed high-quality, secure, and efficient annotation of 50,000 medical images for training their machine learning models. The project aimed to refine annotation guidelines, improve consistency, and achieve a 99%+ accuracy rate through a structured, multi-phase approach.
Key Challenges
.png)
Stringent accuracy requirements: Achieve a minimum of 99% annotation precision
Strict data security compliance: Adhering to HIPAA/GDPR for handling medical data
Restricted access: Annotations could only be performed within the client’s secure infrastructure
Tight deadline: Project completion within 3–4 months
Solutions
We structured the project into 5 phases to ensure high-quality, precise medical image annotation while maintaining data security and efficiency. Each phase addressed specific challenges and progressively refined the annotation process for optimal accuracy.
Refining Annotation Guidelines
Clear criteria for distinguishing pathology vs. normal tissue.
20+ visual examples added to improve clarity.
Standardized approach: Bounding Boxes for small lesions, Polygons for larger ones.
Implementing Secure Annotation Tools
Used CVAT, a Computer Vision Annotation Tool, for secure on-premise deployment.
Enabled VPN authentication and role-based access control.
Iterative Training & QA Process
Pilot annotation phase (3 rounds): 200 → 500 → 1000 images.
Discrepancy rate reduced from 15% → 5% → <1%.
Introduced cross-verification, where multiple annotators reviewed the same images.
Full-Scale Annotation with Two-Tier QA
50,000 images annotated with a 12-15 min average per image.
30% of images reviewed in internal QA checks.
Final validation by medical experts ensured 99.58% accuracy.
Results
Our data labeling services successfully addressed the client's challenges and delivered exceptional results:
High Annotation Accuracy
Achieved 99.58% final accuracy.
Improved AI Training Data
98% IoU overlap with gold-standard data.
High Annotation Accuracy
Achieved 99.58% final accuracy.
External Validation Success
The client submitted the labeled dataset for external validation and received approval from the FDA, demonstrating the high quality and reliability of the annotations.
Workflow Optimization
The annotation process was optimized by 30% through the implementation of an efficient workflow, enhancing productivity.
Exceptional Annotation Quality
Following final quality checks, less than 0.5% of the images required annotation refinements.
The team did an amazing job refining our guidelines and annotating 50,000 images with incredible accuracy. Their attention to detail and efficient process helped us reach a 99.58% accuracy rate. We’re thrilled with the results and look forward to working with them again!
Head of AI Development
Get in touch
To schedule a call with our team, simply complete the form and click “Submit.” We’ll arrange a meeting at the earliest convenient time for you.
Serhiy Smaglyuk, Founder of UTOR
What You Can Expect on the Consultation:
We’ll take the time to understand your unique project needs and challenges.
You’ll get a realistic estimate of how long your project will take.
We’ll provide clear pricing information based on your project’s scope and complexity.
You’ll get a realistic estimate of how long your project will take.
There’s no obligation to move forward. If you're not sure, we won’t rush or push you into any decisions.
UTOR | Rotermanni 6, 10111 Tallinn, Estonia