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.

Generative adversarial networks (GANs) are increasingly used to generate synthetic medical images, addressing the critical shortage of annotated data for training artificial intelligence (AI) systems. This study introduces conditional random field (CRF)-GAN, a novel memory-efficient GAN architecture that enhances structural consistency in 3D medical image synthesis. Integrating conditional random fields (CRFs) within a two-step generation process, allows CRF-GAN improving spatial coherence while maintaining high-resolution image quality. The model is designed to be computationally efficient, avoiding the need for additional GANs or post-processing. Its performance is evaluated against the state-of-the-art hierarchical (HA)-GAN model. We evaluate the performance of CRF-GAN against the state-of-the-art hierarchical (HA)-GAN model. The comparison between the two models was made through a quantitative evaluation, using Fréchet Inception distance (FID) and maximum mean discrepancy (MMD) metrics, and a qualitative evaluation, through a two-alternative forced choice (2AFC) test completed by a pool of 12 resident radiologists, in order to assess the realism of the generated images. CRF-GAN outperformed HA-GAN with lower FID (0.047 vs. 0.061) and MMD (0.084 vs. 0.086) scores, indicating better image fidelity. The 2AFC test showed a significant preference for images generated by CRF-Gan over those generated by HA-GAN with a p-value of 1.93e - 05. Additionally, CRF-GAN demonstrated 9.34% lower memory usage at 256