DeformableGAN: Generating Medical Images With Improved Integrity for Healthcare Cyber Physical Systems

The development of deep learning enables the production of new images via generative adversarial networks (GANs). The GANs have now been widely applied in the industry as well as academic research that brought tremendous progress to our community. Many researchers in medical imaging also introduced this novel technology for medical image reconstruction, segmentation, synthesis, etc. On the other hand, the GAN-generated images may also suffer from exhibiting unique textures, namely the checkerboard artifacts. For medical diagnosis and healthcare, such artifacts could bring negative impacts as they may distort information collected in medical images. Improper treatment and rehabilitation plans based on the disinformation of checkerboard artifacts could be harmful for patients and healthcare cyber physical systems. Thus, we investigate the checkerboard artifact synthesized during adversarial training in this paper. Based on the theoretical analysis, we propose a method for GANs to generate images without producing checkerboard artifacts. It could protect medical images preserving high integrity for healthcare cyber physical systems. Our experiments justify the efficiency of proposed method when associating with a variety of GANs for image synthesis. Also, we prove that it is feasible to detect GAN-generated images by tracing the checkerboard artifacts.

Automated summary

This paper investigates the issue of checkerboard artifacts in GAN-generated medical images and proposes a method to avoid the production of such artifacts. The method preserves the integrity of medical images and demonstrates the feasibility of detecting GAN-generated images by tracing the checkerboard artifacts.