Evaluating Generative Models in Medical Imaging

Authors

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

, "Evaluating Generative Models in Medical Imaging" (2024). Faculty, Staff and Student Publications. 918.
https://digitalcommons.library.tmc.edu/uthshis_docs/918