mlsanityData to 7 Oct 2026

Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network

arXiv 2104.02060 ↗First posted 2 Apr 2021eess.IV cs.CV cs.LGcited by 3

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Abstract (arXiv)

We present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D scan volumes. We believe conditional cGAN to be a tractable approach to generate 3D CT volumes, even though the problem of generating full resolution deep fakes is presently impractical due to GPU memory limitations. We present results for autoencoder, denoising, and depixelating tasks which are trained and tested on two novel COVID19 CT datasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from 12.53 - 46.46 dB, and the Structural Similarity index ( SSIM) range from 0.89 to 1.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 2 Apr 2021.

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11 of 20 references.

References · 11

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2014Conditional Generative Adversarial Netscited by 2,152
  3. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  4. 2020MosMedData: Chest CT Scans With COVID-19 Related Findings Datasetcited by 103
  5. 2020BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patientscited by 81
  6. 2020Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysiscited by 38
  7. 2019CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learningcited by 31
  8. 2020Lung Infection Quantification of COVID-19 in CT Images with Deep Learningcited by 28
  9. 2020Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Datasetcited by 10
  10. 2019Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Imagescited by 8
  11. 2020Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GANcited by 5

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1.

3 papers.

  1. 2022Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Reviewcited by 3
  2. 2023Leveraging GANs for data scarcity of COVID-19: Beyond the hypecited by 3
  3. 2021CCS-GAN: COVID-19 CT-scan classification with very few positive training imagescited by 1