Precision in Pathology: Improving Cancer Detection by 32%
“AnnotationBox didn’t just give us labels; they gave us clinical confidence. Their ability to handle the extreme complexity of gigapixel pathology slides allowed our AI to reach diagnostic accuracy levels we thought were years away. They are an essential part of our FDA approval journey.”
— Dr. Aris Thorne, VP of Medical AI, OncoVision Systems
Challenge
OncoVision’s AI struggled to identify micro-metastases in 100,000 gigapixel Whole Slide Images (WSIs). Generalist annotation firms lacked the medical expertise to distinguish between healthy cells and rare cancer variants, leading to high false-negative rates. The sheer scale of data—over 100,000 slides—created a bottleneck that threatened their clinical trial deadlines and regulatory compliance.
Solution
AnnotationBox deployed a Medical Data Unit (MDU) consisting of board-certified pathologists and medically trained annotators. We implemented a “Triple-Pass Consensus Workflow,” where every slide was reviewed by two experts, with a senior pathologist acting as the final arbiter. Using high-precision semantic segmentation and mitotic figure counting, we provided the model with “ground truth” data that accounted for rare cellular morphologies.
Result
The model achieved a 32% relative increase in diagnostic accuracy, with a specific 40% improvement in detecting early-stage micro-metastases. Annotation throughput increased by 300% compared to the client’s internal team, and the project was completed in a HIPAA-compliant environment, ensuring 100% data security.
Bottom Line Impact
Regulatory Readiness
Operational Efficiency
Life-Saving Precision
You Might Also Like
AnnotationBox and the Large Clothing Retailer
