Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend

Authors

McKell Woodland
Austin Castelo
Mais Al Taie
Jessica Albuquerque Marques Silva
Mohamed Eltaher
Frank Mohn
Alexander Shieh
Suprateek Kundu
Joshua P Yung
Ankit B Patel
Kristy K Brock

Language

English

Publication Date

10-1-2024

Journal

Medical Image Computing and Computer Assisted Intervention – MICCAI 2024

DOI

10.1007/978-3-031-72390-2_9

PMID

40444256

PMCID

PMC12117514

PubMedCentral® Posted Date

10-23-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Fréchet Inception Distance (FID) is a widely used metric for assessing synthetic image quality. It relies on an ImageNet-based feature extractor, making its applicability to medical imaging unclear. A recent trend is to adapt FID to medical imaging through feature extractors trained on medical images. Our study challenges this practice by demonstrating that ImageNet-based extractors are more consistent and aligned with human judgment than their RadImageNet counterparts. We evaluated sixteen StyleGAN2 networks across four medical imaging modalities and four data augmentation techniques with Fréchet distances (FDs) computed using eleven ImageNet or RadImageNet-trained feature extractors. Comparison with human judgment via visual Turing tests revealed that ImageNet-based extractors produced rankings consistent with human judgment, with the FD derived from the ImageNet-trained SwAV extractor significantly correlating with expert evaluations. In contrast, RadImageNet-based rankings were volatile and inconsistent with human judgment. Our findings challenge prevailing assumptions, providing novel evidence that medical image-trained feature extractors do not inherently improve FDs and can even compromise their reliability. Our code is available at https://github.com/mckellwoodland/fid-med-eval.

Keywords

Generative Medical Imaging, Fréchet Inception Distance

Published Open-Access

yes

Recommended Citation

Woodland, McKell; Castelo, Austin; Al Taie, Mais; et al., "Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend" (2024). Faculty and Staff Publications. 5316.
https://digitalcommons.library.tmc.edu/baylor_docs/5316