Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest
Journal:
arXiv
Published Date:
Jan 26, 2025
Abstract
Generative Adversarial Networks (GANs) are increasingly used to generate
synthetic medical images, addressing the critical shortage of annotated data
for training Artificial Intelligence systems. This study introduces CRF-GAN, a
novel memory-efficient GAN architecture that enhances structural consistency in
3D medical image synthesis. Integrating Conditional Random Fields within a
two-step generation process allows CRF-GAN improving spatial coherence while
maintaining high-resolution image quality. The model's performance is evaluated
against the state-of-the-art hierarchical (HA)-GAN model. Materials and
Methods: We evaluate the performance of CRF-GAN against the HA-GAN model. The
comparison between the two models was made through a quantitative evaluation,
using FID and MMD metrics, and a qualitative evaluation, through a
two-alternative forced choice (2AFC) test completed by a pool of 12 resident
radiologists, to assess the realism of the generated images. Results: CRF-GAN
outperformed HA-GAN with lower FID and MMD scores, indicating better image
fidelity. The 2AFC test showed a significant preference for images generated by
CRF-Gan over those generated by HA-GAN. Additionally, CRF-GAN demonstrated
9.34% lower memory usage and achieved up to 14.6% faster training speeds,
offering substantial computational savings. Discussion: CRF-GAN model
successfully generates high-resolution 3D medical images with non-inferior
quality to conventional models, while being more memory-efficient and faster.
The key objective was not only to lower the computational cost but also to
reallocate the freed-up resources towards the creation of higher-resolution 3D
imaging, which is still a critical factor limiting their direct clinical
applicability. Moreover, unlike many previous studies, we combined qualitative
and quantitative assessments to obtain a more holistic feedback on the model's
performance.
