Generating Medical Chest X-Ray Images using Generative Adversarial Network
Keywords:
image generation, generative adversarial network, Data augmentation, Chest X-ray, deep learning.Abstract
Generative adversarial networks (GANs) have opened up new possibilities for medical imaging applications. Specifically, GANs can generate high-quality data without labeled data by competing between generator and discriminator networks. The Gans networks are becoming a standard by improving results in various medical tasks like image registration, reconstruction, augmentation, and translating images between images. In this paper, we propose a new GAN architecture to enhance the chest x-ray dataset for unsupervised pneumonia identification. We demonstrate how the proposed GAN works well with generated medical images as augmented data. Medical imaging data is expensive to label and scarce because of patient privacy issues; furthermore, the data is difficult to collect. The loss output resulted in the discriminator equaling 2.222 and the generator loss equaling 1.160 this means the generator succeeded in creating x-ray images that the discriminator unable to distinguished where they were real or fake. In contrast to approaches that depend primarily on transfer learning, we construct our model from scratch. Our primary emphasis is on image creation. The Nividia GeForce RTX 2060-equipped PC on which the suggested system was built was programmed in python 3.7.
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R. Patgiri, A. Biswas, and P. Roy, Studies in Computational Intelligence 932 Health Informatics: A Computational Perspective in Healthcare. 2021. [Online]. Available: http://www.springer.com/series/7092.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 8110–8119.
X. Yi, E. Walia, and P. Babyn, “Generative adversarial network in medical imaging: A review,” Med. Image Anal., vol. 58, p. 101552, 2019.
A. Madani, M. Moradi, A. Karargyris, and T. Syeda-Mahmood, “Chest x-ray generation and data augmentation for cardiovascular abnormality classification,” in Medical imaging 2018: Image processing, SPIE, 2018, pp. 415–420.
H. M. Mohammed and K. H. Ali, “Generating High-Resolution Chest X-ray Images Using CGAN,” J. Basrah Res.(Sci.), vol. 48, no. 2, pp. 88–101, 2022.
L. Cai, Y. Chen, N. Cai, W. Cheng, and H. Wang, “Utilizing amari-alpha divergence to stabilize the training of generative adversarial networks,” Entropy, vol. 22, no. 4, p. 410, 2020.
Goodfellow et al., “Generative adversarial nets,” Adv. Neural Inf. Process. Syst., vol. 27, 2014.
A. S. Kazeminia et al., “GANs for medical image analysis,” Artif. Intell. Med., vol. 109, p. 101938, 2020.
A. Radford, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv Prepr. arXiv1511.06434, 2015.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv Prepr. arXiv1710.10196, 2017.
H. Zhang et al., “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 5907–5915.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” Adv. Neural Inf. Process. Syst., vol. 29, 2016.
T. Kohlberger et al., “Whole-slide image focus quality: Automatic assessment and impact on ai cancer detection,” J. Pathol. Inform., vol. 10, no. 1, p. 39, 2019.
A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro, “Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection,” Ieee Access, vol. 8, pp. 91916–91923, 2020.
Mescheder L, Nowozin S, Geiger A. "Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks". In International conference on machine learning 2017 Jul 17 (pp. 2391-2400). PMLR.
Melendez et al., “A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest X-rays,” IEEE Trans. Med. Imaging, vol. 34, no. 1, pp. 179–192, 2014.
Deng Z, Zhang H, Liang X, Yang L, Xu S, Zhu J, Xing EP. “Structured generative adversarial networks”. Advances in neural information processing systems. 2017;30.
Seff, Ari, Alex Beatson, Daniel Suo, and Han Liu. "Continual learning in generative adversarial nets." arXiv preprint arXiv:1705.08395 (2017).
D. S. Kermany, M. Goldbaum, W. Cai, C.C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G.Yang, X. Wu, F. Yan, et al., “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell, vol. 172, no. 5, pp. 1122–1131, 2018.
Noori A.Y, Shaker SH, Azeez RA. “Street Scene understanding via Semantic Segmentation Using Deep Learning”. Engineering and Technology Journal. 2022 Apr 1;40(04):588-94. [21] 2023. NN-SVG. [Online]. https://alexlenail.me/NN-SVG/AlexNet.html
D. Mahapatra, B. Bozorgtabar, and R. Garnavi, “Image super-resolution using progressive generative adversarial networks for medical image analysis,” Comput. Med. Imaging Graph., vol. 71, pp. 30–39, 2019, doi: 10.1016/j.compmedimag.2018.10.005.
S. Kora Venu and S. Ravula, “Evaluation of deep convolutional generative adversarial networks for data augmentation of chest x-ray images,” Futur. Internet, vol. 13, no. 1, p. 8, 2020.
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