This paper is in the following e-collection/theme issue:

Imaging Informatics (200) Artificial Intelligence (1421) Data Science (278) Innovations in Clinical Trials and Research Data Management (84) Machine Learning (1501)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23328, first published .
Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test

Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test

Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test

Authors of this article:

Ho Young Park1 Author Orcid Image ;   Hyun-Jin Bae2 Author Orcid Image ;   Gil-Sun Hong1 Author Orcid Image ;   Minjee Kim3 Author Orcid Image ;   JiHye Yun1 Author Orcid Image ;   Sungwon Park4 Author Orcid Image ;   Won Jung Chung4 Author Orcid Image ;   NamKug Kim1, 5 Author Orcid Image
Article Authors Cited by (20) Tweetations (3) Metrics

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  1. Chen J, Chen S, Wee L, Dekker A, Bermejo I. Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review. Physics in Medicine & Biology 2023;68(5):05TR01 View
  2. Jang M, Bae H, Kim M, Park S, Son A, Choi S, Choe J, Choi H, Hwang H, Noh H, Seo J, Lee S, Kim N. Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network. Scientific Reports 2023;13(1) View
  3. Lopes D, Monti G, Burgreen G, Moulin J, dos Santos Bobadilha G, Entsminger E, Oliveira R. Creating High-Resolution Microscopic Cross-Section Images of Hardwood Species Using Generative Adversarial Networks. Frontiers in Plant Science 2021;12 View
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  5. Jang Y, Yoo J, Hong H. Assessment and Analysis of Fidelity and Diversity for GAN-based Medical Image Generative Model. Journal of the Korea Computer Graphics Society 2022;28(2):11 View
  6. Hong K, Cho Y, Kang C, Ahn K, Lee H, Kim J, Hong S, Kim B, Shim E. Lumbar Spine Computed Tomography to Magnetic Resonance Imaging Synthesis Using Generative Adversarial Network: Visual Turing Test. Diagnostics 2022;12(2):530 View
  7. Shin K, Lee J, Lee J, Lee H, Kim J, Byeon J, Jung H, Kim D, Kim N. An Image Turing Test on Realistic Gastroscopy Images Generated by Using the Progressive Growing of Generative Adversarial Networks. Journal of Digital Imaging 2023;36(4):1760 View
  8. Hofmeijer E, Wu S, Vliegenthart R, Slump C, van der Heijden F, Tan C. Artificial CT images can enhance variation of case images in diagnostic radiology skills training. Insights into Imaging 2023;14(1) View
  9. Krishna A, Yenneti S, Wang G, Mueller K. Image factory: A method for synthesizing novel CT images with anatomical guidance. Medical Physics 2024;51(5):3464 View
  10. Asadi F, Angsuwatanakul T, O’Reilly J. Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network. IBRO Neuroscience Reports 2024;16:57 View
  11. Gan H, Ramlee M, Al-Rimy B, Lee Y, Akkaraekthalin P. Hierarchical Knee Image Synthesis Framework for Generative Adversarial Network: Data From the Osteoarthritis Initiative. IEEE Access 2022;10:55051 View
  12. Li X, Liu H, Song X, Marboe C, Brott B, Litovsky S, Gan Y. Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images. Journal of Biomedical Optics 2024;29(03) View
  13. Paproki A, Salvado O, Fookes C. Synthetic Data for Deep Learning in Computer Vision & Medical Imaging: A Means to Reduce Data Bias. ACM Computing Surveys 2024;56(11):1 View
  14. Park S, Han K, Lee J. Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology. La radiologia medica 2024;129(11):1644 View
  15. Deshpande R, Özbey M, Li H, Anastasio M, Brooks F. Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context. IEEE Transactions on Medical Imaging 2024;43(10):3608 View
  16. D N S, Pai R, Bhat S, Pai M M M. Assessment of perceived realism in AI-generated synthetic spine fracture CT images. Technology and Health Care 2024 View

Books/Policy Documents

  1. Saad M, Rehmani M, O’Reilly R. Artificial Intelligence and Cognitive Science. View
  2. Pesaranghader A, Wang Y, Havaei M. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. View