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How GANs assist in Covid-19 pandemic era: a review

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Abstract

Generative Adversarial Networks (GANs) are used in several applications of underrepresented domains. The GAN’s ability to generate data without explicitly modeling the probability distribution enables the synthesis of unlabeled samples and effectively imposes a higher-order consistency. Medical data is costly to aggregate and, in many cases, can risk the patient’s privacy. The high fidelity of generated data by GANs has led many researchers in healthcare to use GANs to accommodate the scarce datasets in the domain. Coronavirus disease 2019 (Covid-19) constitutes one of those cases. As a virus that appeared two years ago, limited information and data exist compared to other phenomena present for decades. Thus, Covid-19 datasets are imbalanced and tend to be biased towards negative Covid-19 cases, as usually, only less than 10% of the data belong to a Covid-19 positive class. This study conducts a detailed review of all eleven existing GAN applications that aid Covid-19 detection. The existing work focuses on the synthesis, segmentation, classification, and saliency map generation of radiological data namely Chest X-rays (CXR) and Computed Tomography (CT). Both imaging data have been reliable information sources for detecting Covid-19. Furthermore, the application of GAN in Covid-19 spread modeling with data assimilation and uncertainty quantification is also explored. We believe that the illustrated findings will provide a clear overview and inspire future innovations in the right direction.

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Abbreviations

ACC:

Highest submissions Accuracy

ACGAN:

Auxiliary Classifier GAN

AUC:

Area under the curve

BDLSTM:

Bidirectional Long Short-term Memory network

CCS-GAN:

Cycle Consistent Segmentation Generative Adversarial Network

cGAN:

Conditional Generative Adversarial Network

CNN:

Convolutional Neural Network

Covid-19:

Coronavirus disease 2019

CT:

Computed Tomography

CXR:

Chest X-rays

DA:

Data Augmentation

DA-PredGAN:

Data Assimilation Predictive GAN

DCGAN:

Deep convolutional generative adversarial network

DI2IN:

Deep image-to-image network

DM:

Dice Metric

DSC:

DICE Similarity Coefficient

FID:

Frechet inception distance

GAN:

Generative Adversarial Network

GeoGAN:

Geometry-Aware Shape Generative Adversarial Network

HD:

Hausdorff Distance

LIR:

Lesion inclusion rate

MAE:

Mean Absolute error

MAS:

Mean anomaly score

NIROM:

Non-intrusive reduced-order model

PCA:

Principal Component Analysis

PHO:

Percentage of High Opacity

PO:

Percentage of Opacity

PredGAN:

Predictive

PSNR:

Peak signal-to-noise ratio

RML:

Randomized maximum likelihood

RMSE:

Root mean square error

ROM:

Reduced-Order Models

RTO:

Randomize-then-optimize

RT-PCR:

Reverse-transcription polymerase chain reaction

SSIM:

Structural similarity index measure

STN:

Spatial transformer network

UQ-PredGAN:

Uncertainty Quantification Predictive GAN

WSS:

Weakly supervised segmentation

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Authors and Affiliations

  1. Faculty of Computing, Engineering and Technology, Asia Pacific University, Kuala Lumpur, Malaysia

    Yahya Sherif Solayman Mohamed Saleh

  2. Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Luleå, Sweden

    Hamam Mokayed, Konstantina Nikolaidou & Lama Alkhaled

  3. Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long, Malaysia

    Hum Yan Chai

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  1. Yahya Sherif Solayman Mohamed Saleh
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  2. Hamam Mokayed
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  3. Konstantina Nikolaidou
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  4. Lama Alkhaled
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  5. Hum Yan Chai
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Correspondence to Hum Yan Chai.

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Appendix

Appendix

1.1 Datasets

COVID-19 CT segmentation dataset. [22]: https://medicalsegmentation.com/covid19/

Covid-19 CXR Database initiative [115]: https://github.com/lindawangg/COVID-Net

COVID-19 image data collection [20]: https://github.com/ieee8023/covid-chestxray-dataset

COVID-CT-Dataset [128]: https://github.com/UCSD-AI4H/COVID-CT

COVIDGR datasets [108]: https://github.com/ari-dasci/OD-covidgr#covidgr-10

COVID-QU-Ex Dataset [109]:https://www.kaggle.com/datasets/anasmohammedtahir/covidqu

Synthetic COVID-19 chest X-ray [138]: https://github.com/hasibzunair/synthetic-covid-cxr-dataset/releases/tag/v0.1

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Saleh, Y.S.S.M., Mokayed, H., Nikolaidou, K. et al. How GANs assist in Covid-19 pandemic era: a review. Multimed Tools Appl 83, 29915–29944 (2024). https://doi.org/10.1007/s11042-023-16597-y

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