Views provided by UsageCounts
Table S1: Summary of performance metrics for section 3.2; Dataset S2: Instance-diverse synthetic COVID-19 CT images generated from the sRD-GAN with light residual dropout; Dataset S3: Synthetic COVID-19 CT images generated from the HUST-19 dataset; Dataset S4: Synthetic CAP CT images generated by sRD-GAN; Dataset S5: Synthetic COVID-19 X-Ray images generated by sRD-GAN. More synthetic COVID-19 CT images can be found at Large-scale Instance-diverse Synthetic COVID-19 CT Dataset Acknowledgements: 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. Bioengineering 2022, 9, 698. https://doi.org/10.3390/bioengineering9110698 References: [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. 2020, 4, 1197–1207.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 20 |

Views provided by UsageCounts