Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation

Zunair, Hasib; Hamza, A. Ben

doi:10.1007/s13278-021-00731-5

Cited by 44 publications

(29 citation statements)

References 30 publications

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“…Yang et al [ 24 ] proposed an extra structure-consistency loss based on the modality of independent neighborhood descriptor to improve CycleGAN for unsupervised MR-to-CT synthesis. Zunair and Hamza [ 25 ] adopted adversarial training and transfer learning to convert normal and pneumonia chest X-ray to COVID-19 chest X-ray. Jiang et al [ 26 ] extended CGAN by employing dual generators and dual discriminators that introduced a dynamic communication mechanism to improve CGAN to synthesize lung computed tomography (CT) images, then combined the generated lung CT images with nonpulmonary CT to get COVID-19 chest X-ray.…”

Section: Related Workmentioning

confidence: 99%

RF‐GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network

Chen

Long

Guo

2021

Computational Intelligence and Neuroscience

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Diabetic retinopathy (DR) is a diabetic complication affecting the eyes, which is the main cause of blindness in young and middle-aged people. In order to speed up the diagnosis of DR, a mass of deep learning methods have been used for the detection of this disease, but they failed to attain excellent results due to unbalanced training data, i.e., the lack of DR fundus images. To address the problem of data imbalance, this paper proposes a method dubbed retinal fundus images generative adversarial networks (RF-GANs), which is based on generative adversarial network, to synthesize retinal fundus images. RF-GANs is composed of two generation models, RF-GAN1 and RF-GAN2. Firstly, RF-GAN1 is employed to translate retinal fundus images from source domain (the domain of semantic segmentation datasets) to target domain (the domain of EyePACS dataset connected to Kaggle (EyePACS)). Then, we train the semantic segmentation models with the translated images, and employ the trained models to extract the structural and lesion masks (hereafter, we refer to it as Masks) of EyePACS. Finally, we employ RF-GAN2 to synthesize retinal fundus images using the Masks and DR grading labels. This paper verifies the effectiveness of the method: RF-GAN1 can narrow down the domain gap between different datasets to improve the performance of the segmentation models. RF-GAN2 can synthesize realistic retinal fundus images. Adopting the synthesized images for data augmentation, the accuracy and quadratic weighted kappa of the state-of-the-art DR grading model on the testing set of EyePACS increase by 1.53% and 1.70%, respectively.

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Section: Related Workmentioning

confidence: 99%

RF‐GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network

Chen

Long

Guo

2021

Computational Intelligence and Neuroscience

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“…Machine Learning (ML), one of the commonly used methods in AI, refers to the intelligence shown by computers [ 10 , 11 ]. Deep Learning (DL) is an improved and scalable ML extension to strengthen the structure of learning algorithms and make them easy to use [ 12 ]. Being a subset of ML, DL has been used tremendously to build complex models with very large data and a simpler setting [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, their use in clinical settings has been limited due to their lack of applicability in real-world scenarios [ 14 ]. While these DL-based approaches are promising, their predictive success relies extensively on the accessibility of high data volumes [ 12 ]. As a consequence, the value of datasets in obtaining results from methods is critical to us.…”

Section: Introductionmentioning

confidence: 99%

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

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Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

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“…A large majority of the proposed solutions tackled disease identification, based on deep learning algorithms [11,[14][15][16][17][18], providing some levels of interpretability [19][20][21]. Other studies approached different tasks like quantification of infection severity [22][23][24], segmentation of image [22,25], prediction of disease evolution [26] and image synthesis [27]. The aim of our study was to design a system for the prioritization, based on COVID-19 infection likelihood, of CXRs to support the diagnostic workflow [28,29].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results

et al. 2022

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The aim of our study is the development of an automatic tool for the prioritization of COVID-19 diagnostic workflow in the emergency department by analyzing chest X-rays (CXRs). The Convolutional Neural Network (CNN)-based method we propose has been tested retrospectively on a single-center set of 542 CXRs evaluated by experienced radiologists. The SARS-CoV-2 positive dataset (n = 234) consists of CXRs collected between March and April 2020, with the COVID-19 infection being confirmed by an RT-PCR test within 24 h. The SARS-CoV-2 negative dataset (n = 308) includes CXRs from 2019, therefore prior to the pandemic. For each image, the CNN computes COVID-19 risk indicators, identifying COVID-19 cases and prioritizing the urgent ones. After installing the software into the hospital RIS, a preliminary comparison between local daily COVID-19 cases and predicted risk indicators for 2918 CXRs in the same period was performed. Significant improvements were obtained for both prioritization and identification using the proposed method. Mean Average Precision (MAP) increased (p < 1.21 × 10−21 from 43.79% with random sorting to 71.75% with our method. CNN sensitivity was 78.23%, higher than radiologists’ 61.1%; specificity was 64.20%. In the real-life setting, this method had a correlation of 0.873. The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence.

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Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation

Cited by 44 publications

References 30 publications

RF‐GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network

RF‐GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results

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