Abstract
Single image super-resolution (SISR) methods aim to generate a high-resolution image from the corresponding low-resolution images. Such methods may be useful in improving the resolution of medical images including chest x-rays. Medical images with superior resolution may subsequently lead to an improved diagnosis. However, SISR methods for medical images are relatively rare. We propose a SISR method for chest x-ray images. Our method uses multi-level information rendering by utilizing the cue about the abnormality present in the images. Experiments on publicly available datasets show the superiority of the proposed method over several state-of-the-art approaches.
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References
Bing, X., Zhang, W., Zheng, L., Zhang, Y.: Medical image super resolution using improved generative adversarial networks. IEEE Access 7, 145030–145038 (2019)
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)
Zhang, S., Liang, G., Pan, S., Zheng, L.: A fast medical image super resolution method based on deep learning network. IEEE Access 7, 12319–12327 (2018)
Mahapatra, D., Bozorgtabar, B., Garnavi, R.: Image super-resolution using progressive generative adversarial networks for medical image analysis. Comput. Med. Imaging Graph. 71, 30–39 (2019)
Qiu, D., Zheng, L., Zhu, J., Huang, D.: Multiple improved residual networks for medical image super-resolution. Futur. Gener. Comput. Syst. 116, 200–208 (2021)
Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.-H., Liao, Q.: Deep learning for single image super-resolution: a brief review. IEEE Trans. Multimedia 21(12), 3106–3121 (2019)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–44 (2020)
Wang, X., et al.: Esrgan: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: making VGG-style convnets great again. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13733–13742 (2021)
Jolicoeur-Martineau, A.: The relativistic discriminator: a key element missing from standard GAN. arXiv preprint arXiv:1807.00734. 2 July 2018
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE CVPR, vol. 7, p. 46 (2017)
Irvin, J., et al.: Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 590–597, 17 July 2019
Nguyen, H.Q., et al.: VinDr-CXR: an open dataset of chest x-rays with radiologist’s annotations. Scientific Data. 9(1), 429 (2022)
Zhang, K., Gool, L.V., Timofte, R.: Deep unfolding network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3217–3226 (2020)
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The authors thank the National Institutes of Health Clinical Center for providing the NIH dataset.
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Yadagiri, V.V., Reddy, S., Paul, A. (2024). Anomaly Guided Generalizable Super-Resolution of Chest X-Ray Images Using Multi-level Information Rendering. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds) Deep Generative Models. MICCAI 2023. Lecture Notes in Computer Science, vol 14533. Springer, Cham. https://doi.org/10.1007/978-3-031-53767-7_8
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DOI: https://doi.org/10.1007/978-3-031-53767-7_8
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