LLM Trainer
Coronary X-ray Angiography Annotation project involving structured labeling of cardiac catheterization imaging data to support AI model development for cardiovascular diagnostics. The project focused on annotating angiographic images and video sequences by identifying coronary arteries and their major branches, delineating vessel lumen boundaries, marking stenotic segments, and classifying the severity of coronary artery narrowing according to standardized clinical criteria. It included segmentation, bounding, and region-of-interest labeling tasks, along with rigorous quality control and inter-annotator validation to ensure high dataset accuracy, consistency, and clinical relevance for training and evaluating deep learning models in automated detection and assessment of coronary artery disease.