Medical Imaging AI Annotation & Quality Assurance Specialist (Radiologist)
Performed high-level medical image annotation and quality assessment for AI model development at LPIXEL Inc. over a period exceeding two years. Contributed to multiple clinical domains, including: Cerebral infarction (MRI-based lesion detection and segmentation) Lung nodule detection (CT-based identification and characterization) Ischemia evaluation (multi-modality imaging interpretation) Key responsibilities included: Precise lesion annotation and segmentation on CT and MRI datasets Image-level and pixel-level labeling for supervised learning models Comprehensive quality assurance (QA) and validation of annotated datasets Development and refinement of gold-standard annotation guidelines to ensure consistency across annotators Collaboration with AI engineers and data scientists to improve model performance and clinical relevance Project scale involved large, multi-case imaging datasets, requiring strict adherence to clinical accuracy and reproducibility standards. All annotations were performed in accordance with radiological best practices, ensuring high-quality training data suitable for advanced AI model development.