Research on synthetic tuberculosis chest X-ray generation using diffusion and GAN models, with downstream classifier experiments, realism metrics, and privacy-oriented evaluation.
Methods, figures, and outcomes from graduate work in medical imaging, evaluation, and applied machine learning.
Research on synthetic tuberculosis chest X-ray generation using diffusion and GAN models, with downstream classifier experiments, realism metrics, and privacy-oriented evaluation.
A chest X-ray research pipeline combining tuberculosis classification, lung segmentation, and diffusion generation, with a current focus on segmentation model comparison and efficiency tradeoffs.
An applied research project for Nevvon that modeled application freezes through session analysis, stuckness heuristics, and predictive factor discovery.
Research and implementation work focused on making healthcare AI systems more interpretable, testable, and communicable to collaborators.