Clinician-Reviewed Annotations
Every image is reviewed by licensed physicians and trained medical professionals using AI-assisted annotation tools for maximum accuracy.
Project Objective:
This project delivers a streamlined annotation workflow tailored for medical AI training and validation. The goal is to ensure clinically accurate labeling while maintaining consistency, scalability, and compliance throughout the process.
Annotation Elements:
Clinician-led annotations by radiologists and pathologists
Support for segmentation masks, bounding boxes, and classifications
QA reviews to ensure inter-rater consistency
Integration with MONAI Label and custom labeling tools
Process Highlights:
Initial review of imaging types and annotation scope
Labeling guidelines established with clinical input
Iterative annotation cycles with embedded QA checks
Final export in formats ready for AI model ingestion (DICOM-RT, NIfTI, JSON)
By using real-time annotation tools and clinical feedback loops, the process ensures both speed and precision — delivering datasets that are AI-ready and regulatory-aligned.
