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Li D, De L, Keqing L, Gjoni G. RETRACTED ARTICLE: Extreme Learning Machine (ELM) Method for Classification of Preschool Children Brain Imaging. J Autism Dev Disord 2024; 54:1625. [PMID: 36881259 DOI: 10.1007/s10803-022-05891-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/27/2022] [Indexed: 03/08/2023]
Affiliation(s)
- Deming Li
- School of Education, Jilin International Studies University, Changchun130117, Jilin, China.
| | - Li De
- Hunan Institute of Technology, Hengyang, China
| | - Li Keqing
- School of Science and Engineering, City University of Hong Kong, Hong Kong, China
| | - Gazmir Gjoni
- Autonomous University of Zacatecas, Zacatecas, Mexico
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Habashi AG, Azab AM, Eldawlatly S, Aly GM. Generative adversarial networks in EEG analysis: an overview. J Neuroeng Rehabil 2023; 20:40. [PMID: 37038142 PMCID: PMC10088201 DOI: 10.1186/s12984-023-01169-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 03/30/2023] [Indexed: 04/12/2023] Open
Abstract
Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications.
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Affiliation(s)
- Ahmed G Habashi
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt
| | - Ahmed M Azab
- Biomedical Engineering Department, Technical Research Center, Cairo, Egypt
| | - Seif Eldawlatly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt.
- Computer Science and Engineering Department, The American University in Cairo, Cairo, Egypt.
| | - Gamal M Aly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt
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Cheng D, Chen C, Yanyan M, You P, Huang X, Gai J, Zhao F, Mao N. Self-supervised learning for modal transfer of brain imaging. Front Neurosci 2022; 16:920981. [PMID: 36117623 PMCID: PMC9477095 DOI: 10.3389/fnins.2022.920981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/08/2022] [Indexed: 11/19/2022] Open
Abstract
Today's brain imaging modality migration techniques are transformed from one modality data in one domain to another. In the specific clinical diagnosis, multiple modal data can be obtained in the same scanning field, and it is more beneficial to synthesize missing modal data by using the diversity characteristics of multiple modal data. Therefore, we introduce a self-supervised learning cycle-consistent generative adversarial network (BSL-GAN) for brain imaging modality transfer. The framework constructs multi-branch input, which enables the framework to learn the diversity characteristics of multimodal data. In addition, their supervision information is mined from large-scale unsupervised data by establishing auxiliary tasks, and the network is trained by constructing supervision information, which not only ensures the similarity between the input and output of modal images, but can also learn valuable representations for downstream tasks.
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Affiliation(s)
- Dapeng Cheng
- School of Computer Science and Technology, Shandong Business and Technology University, Yantai, China
- Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China
| | - Chao Chen
- School of Computer Science and Technology, Shandong Business and Technology University, Yantai, China
| | - Mao Yanyan
- School of Computer Science and Technology, Shandong Business and Technology University, Yantai, China
- College of Oceanography and Space Informatics, China University of Petroleum, Qingdao, China
| | - Panlu You
- School of Computer Science and Technology, Shandong Business and Technology University, Yantai, China
| | - Xingdan Huang
- School of Statistics, Shandong Business and Technology University, Yantai, China
| | - Jiale Gai
- School of Computer Science and Technology, Shandong Business and Technology University, Yantai, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Business and Technology University, Yantai, China
- Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
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Ali H, Biswas R, Ali F, Shah U, Alamgir A, Mousa O, Shah Z. The role of generative adversarial networks in brain MRI: a scoping review. Insights Imaging 2022; 13:98. [PMID: 35662369 PMCID: PMC9167371 DOI: 10.1186/s13244-022-01237-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/11/2022] [Indexed: 11/23/2022] Open
Abstract
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.
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Affiliation(s)
- Hazrat Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
| | - Rafiul Biswas
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Farida Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Uzair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Asma Alamgir
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Osama Mousa
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
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Generative Adversarial Networks in Brain Imaging: A Narrative Review. J Imaging 2022; 8:jimaging8040083. [PMID: 35448210 PMCID: PMC9028488 DOI: 10.3390/jimaging8040083] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/08/2022] [Accepted: 03/15/2022] [Indexed: 02/04/2023] Open
Abstract
Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases. Generative Adversarial Networks have been proposed as one of the most exciting applications of deep learning in radiology. GANs are a new approach to deep learning that leverages adversarial learning to tackle a wide array of computer vision challenges. Brain radiology was one of the first fields where GANs found their application. In neuroradiology, indeed, GANs open unexplored scenarios, allowing new processes such as image-to-image and cross-modality synthesis, image reconstruction, image segmentation, image synthesis, data augmentation, disease progression models, and brain decoding. In this narrative review, we will provide an introduction to GANs in brain imaging, discussing the clinical potential of GANs, future clinical applications, as well as pitfalls that radiologists should be aware of.
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