RS-FFGAN:Generative adversarial network based on real sample feature fusion for pediatric CXR image data enhancement

Guo, Zhengzheng; Zheng, Lixin; Huang, Detian; Tan, Yan; Su, Qiu-Ling

doi:10.1016/j.jrras.2022.100461

Cited by 2 publications

(32 citation statements)

References 11 publications

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“…This increasing trend was in line with the one in radiology [ 1 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 ]. About half of the articles ( n = 17) were journal papers [ 77 , 78 , 82 , 84 , 87 , 90 , 92 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 105 , 109 , 111 ]. Around two-...…”

Section: Resultssupporting

confidence: 87%

“…All identified articles were published over the last five years and the publication number increased every year with the highest number in 2022 [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 ]. This increasing trend was in line with the one in radiology [ 1 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ,…”

Section: Resultsmentioning

confidence: 99%

“…About half of the articles ( n = 17) were journal papers [ 77 , 78 , 82 , 84 , 87 , 90 , 92 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 105 , 109 , 111 ]. Around two-thirds of these ( n = 11) were determined as being of high quality [ 82 , 84 , 87 , 90 , 92 , 97 , 102 , 103 , 105 , 109 , 111 ]. All low-quality ones were conference papers ( n = 12) [ 79 , 80 , 81 , 83 , 85 , 86 , 91 , 93 , 94 , 95 , 104 , 108 ].…”

Section: Resultsmentioning

confidence: 99%

“…All low-quality ones were conference papers ( n = 12) [ 79 , 80 , 81 , 83 , 85 , 86 , 91 , 93 , 94 , 95 , 104 , 108 ]. The GAN was commonly applied to MRI ( n = 18) [ 77 , 78 , 83 , 84 , 87 , 90 , 97 , 101 , 103 , 104 , 105 , 106 , 108 , 109 , 110 , 111 , 112 , 113 ] and X-ray ( n = 13) [ 79 , 80 , 89 , 91 , 92 , 94 , 95 , 96 , 98 , 99 , 100 , 102 , 107 ], and the others included computed tomography (CT) ( n = 4) [ 82 , 86 , 93 , 97 ], ultrasound ( n = 2) [ 85 , 88 ] and positron emission tomography (PET) ( n = 1) [ 81 ]. Although the basic GAN architecture was still popular among the included studies ( n = 11) [ 77 …”

Section: Resultsmentioning

confidence: 99%

“…Both image synthesis and data augmentation ( n = 12) [ 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 ], and image translation ( n = 12) [ 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 ] were the commonest application areas of GAN in pediatric radiology. Other GAN application areas included image segmentation ( n = 5) [ 84 , 85 , 86 , 87 , 88 ], image reconstruction ( n = 4) [ 80 , 81 , 82 , 83 ], disease diagnosis ( n = 3) [ 77 , 78 , 79 ], and image quality assessment ( n = 1) [ 113 ].…”

Section: Resultsmentioning

confidence: 99%

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Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

2023

Children

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Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1–158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

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