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Synthetic data augmentation using GAN for improved liver lesion classification
Open accessAbstract
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.
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Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., & Bray, F. (2015) · International Journal of Cancer · doi:10.1002/ijc.29210 · Cited by 28,766
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
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Dastile, X., Celik, T., & Potsane, M. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106263 · Cited by 404
Shmelkov, K., Schmid, C., & Alahari, K. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_14 · Cited by 341
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Chen, L., Zhang, Y., Song, Y., Liu, L., & Wang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01815 · Cited by 307
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Metadata
- Authors
- Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H.
- Venue
- 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 289-293
- OpenAlex
- W2784331504
- Cited by
- 860
- References
- 2
- FWCI
- 26.38
- Type
- conference paper
- Language
- en
- Open access
- green
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- Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H. (2018). Synthetic data augmentation using GAN for improved liver lesion classification. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). https://doi.org/10.1109/isbi.2018.8363576
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