Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT

Uemura, Tomoki; Näppi, Janne J.; Watari, Chinatsu; Hironaka, Tetsuo; Kamiya, Tohru; Yoshida, Hiroyuki

doi:10.1016/j.media.2021.102159

Cited by 14 publications

(17 citation statements)

References 49 publications

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“… TL? Scarpiniti, Sarv Ahrabi [ 68 ] A histogram-based 90.1 90.3 90.4 91 No No Perumal, Narayanan [ 69 ] CNN 92.3 91.5 92.6 93 No No Uemura, Näppi [ 70 ] GAN 95.1 95.4 96 95.3 No No Zhao, Xu [ 71 ] 3D V-Net 97.4 97.7 97.2 98.7 No No Hu, Huang [ 72 ] DNN 97.2 97.1 98.2 99 No No Toğaçar, Muzoğlu [ 73 ] CNN 97.6 97.3 98.1 99.1 No No Castiglione, Vijayakumar [ 74 ] ADECO-CNN 98.2 98.6 98.4 99 No Yes …”

Section: Resultsmentioning

confidence: 99%

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

Heidari

Toumaj

Navimipour

et al. 2022

Computers in Biology and Medicine

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Section: Resultsmentioning

confidence: 99%

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

Heidari

Toumaj

Navimipour

et al. 2022

Computers in Biology and Medicine

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“…As the diagnosis of COVID-19 using medical imaging has been a priority since the pandemic started, 39 (68%) of 57 studies reported the diagnosis of COVID-19 as the main focus of their work [ 19 - 21 , 23 - 33 , 35 - 37 , 39 , 41 , 42 , 44 , 46 , 50 , 52 , 53 , 55 , 56 , 58 - 60 , 63 - 69 , 71 , 72 ]. In addition, 9 (16%) studies reported data augmentation as the main task addressed in the work [ 18 , 43 , 45 , 49 , 54 , 61 , 62 ], 1 (2%) study reported prognosis of COVID-19 [ 22 ], 3 (5%) studies reported segmentation of lungs [ 34 , 51 , 57 ], and 1 (2%) study reported diagnosis of multiple lung diseases [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

“…The augmented data were then used to improve the training of different CNNs to diagnose COVID-19. In addition, 3 (5%) studies used GANs for segmentation of the lung region within the chest radiology images [ 37 , 51 , 57 ], 3 (5%) studies used GANs for superresolution to improve the quality of the images before using them for diagnosis purposes [ 30 , 44 , 68 ], 5 (9%) studies used GANs for the diagnosis of COVID-19 [ 20 , 58 , 69 , 70 , 72 ], 2 (4%) studies used GANs for feature extraction from images [ 19 , 47 ], and 1 (2%) study used a GAN-based method for prognosis of COVID-19 [ 22 ]. The prevalent mode of imaging is the use of 2D imaging data, and 1 (2%) study reported a GAN-based method for synthesizing 3D data [ 49 ].…”

Section: Resultsmentioning

confidence: 99%

“…The cycleGAN is an image translation GAN that does not require paired data to transform images from one domain to another. Other popular types of GANs were conditional GAN used by 9 (16%) studies [ 18 , 22 , 24 , 25 , 33 , 37 , 41 , 57 , 60 ], deep convolutional GAN used by 4 (7%) studies [ 21 , 38 , 43 , 67 ], and auxiliary classifier GAN used by 4 (7%) studies [ 32 , 40 , 55 , 69 ]. The superresolution GAN was used by 2 (4%) studies [ 44 , 68 ], and 1 (2%) study reported the use of multiple GANs, namely Wassertein GAN, auxiliary classifier GAN, and deep convolutional GAN, and compared their performances for improving the quality of images [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, the use of X-ray images dominated the studies. In total, 29 (51%) studies used X-ray images of lungs [ 20 , 21 , 25 , 27 - 29 , 31 , 32 , 35 , 37 , 40 - 43 , 45 , 50 , 52 , 54 , 56 , 57 , 59 , 60 , 62 , 64 , 65 , 67 , 70 , 73 , 74 ], while 21 (37%) studies used CT images [ 18 , 19 , 22 - 24 , 26 , 30 , 33 , 34 , 36 , 38 , 48 , 49 , 51 , 53 , 55 , 58 , 61 , 63 , 66 , 71 ], and 6 (11%) studies reported the use of both X-ray and CT images [ 39 , 44 , 46 , 47 , 68 , 72 ]. Only 1 (2%) study used ultrasound images for COVID-19 diagnosis [ 69 ], which shows that ultrasound is not a popular imaging modality for training GANs and other deep learning models for COVID-19 detection (also see Figure 4 ).…”

Section: Resultsmentioning

confidence: 99%

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Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

Ali¹,

Shah²

2022

JMIR Med Inform

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Background Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. Objective This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. Methods A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. Results This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. Conclusions Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to i...

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Machine learning applications for COVID-19 outbreak management

Heidari

Navimipour

Ünal

et al. 2022

Neural Comput & Applic

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Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

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Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT

Cited by 14 publications

References 49 publications

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

Machine learning applications for COVID-19 outbreak management

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