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Recent studies have shown that synthetic COVID-19 chest X-rays can be generated from randomly distributed noise vectors using the auxiliary classifier GAN (ACGAN) [22]. Unlike conventional GANs, the generator of the ACGAN generates the output images from the input noise distribution and the class label of the target distribution. The discriminator receives the generated images and predicts the probability that the image belongs to a specific class and that the input image is real. The original ACGAN study claimed that adding class labels as an input can improve the stability of the training dynamics and enhance the quality of the synthetic images [44]. Although ACGAN does not rely on image pair supervision, in image synthesization it is inevitably challenging to generate high-frequency and detailed structures from noise distribution. Consequently, the GGO features presented in lower resolutions can be misinterpreted as bronchi, bronchioles, or other unrelated abnormalities.
CycleGAN is another exciting extension of GANs, and is trained on unpaired images. It encourages the outputs of the reverse generator to be similar to the input of the forward generator, which forms a cycle-consistency constraint that regularizes the bidirectional image translation dynamics and enhances the quality of the synthetic images [45]. The attractive property of the CycleGAN training algorithm essentially addresses the problem of low image-quality synthesization and the requirement of image pair supervision. CycleGAN has demonstrated promising results in high-quality COVID-19 CT image synthesization from lung cancer images [21]. The authors of the study noted that the image translation takes advantage of the existing annotations from the lung cancer nodules of the lung cancer images to generate plausible features of COVID-19 around the location of the nodules. A similar approach was also found to adapt well to COVID-19 chest X-ray images, and was intended to reduce the class imbalance distribution of COVID-19 X-ray images compared to CAP and health control in a COVID-19 classification task [23].
Most existing COVID-19 imaging modality synthetization approaches generate deterministic outputs, which means only a single output can be generated from every unique input [21,22,23,28,42]. This is a considerable drawback for data augmentation due to the limited image diversity from the deterministic setting of the GANs. By comparison, most multimodal GAN frameworks aim to maximize perceptual diversity via disentangled representations, which results in significant perceptual differences [38,39]. As such, image translation for medical images that relies on subtle and detailed visual descriptions of the radiographic findings is more appropriately formulated using a fine-grained feature transfer approach. In particular, fine-grained image translation aims to transfer only the fine contexture details of the images. However, fine-grained feature transfer approaches lack image quality preservation due to the sizeable geometrical deformation and dependency on Variational Autoencoders (VAEs), which are prone to generate blurry images [40,41].
The utilization of regularization techniques to encourage image diversity has been investigated previously using Gaussian noise addition and dropout layers in U-net architecture [46]. However, the stochasticity induced by the proposed regularization strategy did not generate perceptually significant structural variances in the image transformation tasks. In recent work, Yang et al. regulated the generator using a maximization objective conditioned on two randomly sampled noise latent codes [47]. Although the proposed method was explicitly designed for cGAN algorithms, the capability of the method in facilitating fine-grained feature diversity and its effectiveness in unsupervised training algorithms remain obscure.
where yact denotes the activation output from the final residual block, and x is the final down-sampled input to the image transformation network. The padding, normalization, and activation layers are excluded from the formula for simplicity. Figure 2 illustrates the configuration of the image transformation network in both training and inference modes.
Image transformation network incorporated with the residual dropout mechanism in the training mode and the inference mode. The RD-activation code illustrates the reconfiguring of the residual dropout at the inference mode to amplify the latent space stochasticity without any model retraining.
In contrast to conventional loss functions, for which importance is controlled by constant values, the proposed adaptive setting of the pixel consistency loss function changes the importance of the loss based on the generator loss at each training step. Therefore, a smaller error resulting from the generator will correspond to a smaller weight update. The pixel consistency loss for both image mapping is defined as:
In addition, it is found that the synthetic images generated by the GAN model suffer significant noise interference and a large magnitude of information distortion. The background noises appeared as large spots that are greyish-white in color. Moreover, the synthetic GGO features are presented in blurry conditions, with hazy lines and border structures. By comparison, images generated by the CycleGAN model improve drastically in terms of perceptual quality, which is also indicated by the lower FID score. The improvement is attributed to the bidirectional mapping mechanism that enforces a cycle-consistency constraint between the image domains at the cost of doubling the training duration to 106.67 h, compared to 58.67 h with GAN. Since sRD-GAN and one-to-one CycleGAN are trained with cycle-consistency loss, the training duration is similar to that of the original CycleGAN, at 106.67 h, despite only one generator model being used.
Similar to [36], only a single generator model is used in the GAN framework. The generator model consists of three major components: two down-sampling blocks, nine residual connection blocks, and two up-sampling blocks. The input dimension of the generator model is fixed at 256 256 3 and is the same for output images.
The discriminator is referenced from the pix2pix architecture [46]. PatchGAN maps an input distribution to the variable size of NN arrays instead of generating outputs of a single scalar output. Therefore, each value of the squared activation outputs represents a likelihood probability that the input image is real. The value chosen for N in this study was fixed as 70, which is based on the recommendation of the authors of the paper. Details of the model can be found in the original paper of pix2pix GAN [46]. 2ff7e9595c
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