The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Image super-resolution using progressive generative adversarial networks for medical image analysis
Abstract
Anatomical landmark segmentation and pathology localisation are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images (e.g. vasculature branches or microaneurysm lesions) and cardiac MRI, or when the image is of low quality due to device acquisition parameters as in magnetic resonance (MR) scanners. We propose an image super-resolution method using progressive generative adversarial networks (P-GANs) that can take as input a low-resolution image and generate a high resolution image of desired scaling factor. The super resolved images can be used for more accurate detection of landmarks and pathologies. Our primary contribution is in proposing a multi-stage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function. The triplet loss enables stepwise image quality improvement by using the output of the previous stage as the baseline. This facilitates generation of super resolved images of high scaling factor while maintaining good image quality. Experimental results for image super-resolution show that our proposed multi stage P-GAN outperforms competing methods and baseline GANs. The super resolved images when used for landmark and pathology detection result in accuracy levels close to those obtained when using the original high resolution images. We also demonstrate our methods effectiveness on magnetic resonance (MR) images, thus establishing its broader applicability.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Labaca-Castro, R. (2023) · Machine Learning under Malware Attack · doi:10.1007/978-3-658-40442-0_9 · Cited by 19,272
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Compression Artifacts Reduction by a Deep Convolutional Network
Dong, C., Deng, Y., Loy, C. C., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.73 · Cited by 883
Fast and accurate image upscaling with super-resolution forests
Schulter, S., Leistner, C., & Bischof, H. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299003 · Cited by 618
Cited by
A Review of the Deep Learning Methods for Medical Images Super Resolution Problems
Li, Y., Sixou, B., & Peyrin, F. (2021) · IRBM · doi:10.1016/j.irbm.2020.08.004 · Cited by 281
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Wang, Z., She, Q., & Ward, T. E. (2022) · ACM Computing Surveys · doi:10.1145/3439723 · Cited by 267
Özyurt, F., Sert, E., & Avcı, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109433 · Cited by 210
Martín Noguerol, T., Paulano-Godino, F., Martín-Valdivia, M. T., Menias, C. O., & Luna, A. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.047 · Cited by 175
Shobayo, O., & Saatchi, R. (2025) · Diagnostics · doi:10.3390/diagnostics15091072 · Cited by 53
Metadata
- Authors
- Mahapatra, D., Bozorgtabar, B., & Garnavi, R.
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 71
- Pages
- 30-39
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2901354392
- Cited by
- 308
- References
- 21
- FWCI
- 13.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mahapatra, D., Bozorgtabar, B., & Garnavi, R. (2019). Image super-resolution using progressive generative adversarial networks for medical image analysis. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2018.10.005
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