Evaluating Generative Models in Medical Imaging

Data synthesis can address important data availability challenges in biomedical informatics. Quantitative evaluation of generative models may help understand their applications to synthesizing biomedical data. This poster paper examines state-of-the-art generative models used in medical imaging, such as StyleGAN and DDPM models, and evaluates their performance in learning data manifolds and in the visible features of generated samples. Results show that existing generative models have much to improve based on the studied measures.