Large-scale Instance-diverse Synthetic COVID-19 CT Dataset

<strong>Dataset Description</strong> This dataset consists of 367000 synthetic COVID-19 CT images generated from a new GAN algorithm known as the stacked residual dropout GAN (sRD-GAN) [1]. The 367 input images are acquired from the iCTCF dataset [2] and are stored in 512 × 512 × 3 in .jpg format. The input images [2] and their corresponding synthetic images are included in this dataset. <strong>Stacked Residual Dropout GAN (sRD-GAN)</strong> The sRD-GAN utilizes a regularization-based strategy in an Image-to-Image (I2I) translation setting to facilitate instance-level diversity. In this study, we show that the stacked dropout regularization in the generator model can induce significant latent-space stochasticity which generates perceptually significant structural dissimilarity in the output space. Paper: Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout DOI: 10.3390/bioengineering9110698 <strong>Advantages</strong> 1) High-resolution, diverse patterns of synthetic ground-glass-opacities presented in chest CT images with different anatomy. 2) Generalize across GAN-based models since the stacked residual dropout mechanism is not task- or dataset-specific. 3) Does not require any auxiliary condition to generate diverse outputs. 4) Does not require any non-trivial modification on the model's architectures. <strong>Disadvantages</strong> 1) Diversity is not presented in large perceptual differences, and it focuses only on fine-grained details. 2) Since sRD-GAN is trained in an unsupervised image-to-image setting, and the synthesis process does not require any auxiliary condition, thus, the magnitude of the style attributes (GGO features) cannot be manipulated. <strong>Acknowledgments: </strong> If you use this dataset in your research, please credit the author: [1] Lee, K.W.; Chin, R.K.Y. Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout. <em>Bioengineering</em> <strong>2022</strong>, <em>9</em>, 698. https://doi.org/10.3390/bioengineering9110698 <strong>References: </strong> [2] Ning, W.; Lei, S.; Yang, J.; Cao, Y.; Jiang, P.; Yang, Q.; Zhang, J.; Wang, X.; Chen, F.; Geng, Z.; et al. Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via Deep Learning. Nat. Biomed. Eng. <strong>2020</strong>, 4, 1197–1207.