Research on the Modality Transfer Method of Brain Imaging Based on Generative Adversarial Network

Cheng, Dapeng; Qiu, Nuan; Zhao, Feng; Mao, Yanyan; Li, Chengnuo

doi:10.3389/fnins.2021.655019

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…While there are many different types of GANs usually named based on their architectures, there is a tendency to assign a new name to every GAN even if the fundamental changes in the architecture are not significant. This review found that the most common types of GANs used were the cycleGAN used by 12 studies [ 15 , 17 , 48 , 51 , 55 – 57 , 65 , 66 , 79 , 84 , 110 , 133 ] followed by conditional GAN used by 8 studies [ 53 , 54 , 71 , 72 , 101 , 112 , 118 , 119 ], and Wasserstein GAN used by 7 studies [ 13 , 14 , 19 , 39 , 116 , 131 , 132 ]. Other types of GANs reported in more than one study were deep convolutional GAN, reported in three studies [ 20 , 93 , 140 ], unified GAN [ 21 , 49 ] reported in two studies, and Pix2Pix GAN, reported in two studies [ 32 , 133 ].…”

Section: Resultsmentioning

confidence: 99%

The role of generative adversarial networks in brain MRI: a scoping review

et al. 2022

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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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Section: Resultsmentioning

confidence: 99%

The role of generative adversarial networks in brain MRI: a scoping review

et al. 2022

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“…GANs have demonstrated success in augmenting EEG data for motor imagery, P300-based applications, emotion recognition, and epileptic seizure detection and prediction. It is noteworthy here that a few studies on EEG-based image generation have been excluded from this article such as [ 95 – 98 ], and [ 99 , 100 ]. The main justification for this elimination is that these studies mainly use GAN methods for image generation and EEG signals were being used as an auxiliary input without applying GAN to the EEG data itself.…”

Section: Discussionmentioning

confidence: 99%

Generative adversarial networks in EEG analysis: an overview

Habashi

Azab

Eldawlatly

et al. 2023

J NeuroEngineering Rehabil

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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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“…There are multiple potential benefits to this, including decreased need for multimodal studies, reduced acquisition time and radiation exposure, and increased availability of appropriate imaging in cases where there is limited access to multimodal imaging, e.g., in a clinical scenario where a certain imaging modality (such as MRI) might be optimal for diagnosis or treatment planning but only another modality (such as CT) is available to the patient. The predominant type of GAN used for this application is CycleGAN [21,[90][91][92][93].…”

Section: Image-to-image Translationmentioning

confidence: 99%

Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review

Cekić

2024

Artificial Intelligence

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Anomaly detection in medical data is often of critical importance, from diagnosing and potentially localizing disease processes such as epilepsy to detecting and preventing fatal events such as cardiac arrhythmias. Generative adversarial networks (GANs) have since their inception shown promise in various applications and have been shown to be effective in cybersecurity, data denoising, and data augmentation, and have more recently found a potentially important place in the detection of anomalies in medical time series. This chapter provides a selective review of this novel use of GANs, in the process highlighting the nature of anomalies in time series, special challenges related to medical time series, and some general issues in approaching time series anomaly detection with deep learning. We cover the most frequently applied GAN models and briefly detail the current landscape of applying GANs to anomaly detection in two commonly used medical time series, electrocardiography (ECG) and electroencephalography (EEG).

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Research on the Modality Transfer Method of Brain Imaging Based on Generative Adversarial Network

Cited by 8 publications

References 26 publications

The role of generative adversarial networks in brain MRI: a scoping review

The role of generative adversarial networks in brain MRI: a scoping review

Generative adversarial networks in EEG analysis: an overview

Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review

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