Faculty, Staff and Student Publications
Language
English
Publication Date
6-1-2024
Journal
Proceedings of the IEEE International Conference on Healthcare Informatics
DOI
10.1109/ichi61247.2024.00084
PMID
39464171
PMCID
PMC11508590
PubMedCentral® Posted Date
8-22-2025
PubMedCentral® Full Text Version
Author MSS
Abstract
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.
Keywords
Medical imaging, generative models, data synthesis
Published Open-Access
yes
Recommended Citation
Liyue Fan, Ashley Bang, and Luca Bonomi, "Evaluating Generative Models in Medical Imaging" (2024). Faculty, Staff and Student Publications. 918.
https://digitalcommons.library.tmc.edu/uthshis_docs/918