Abstract
A serious issue with deep learning in medical applications is the limited availability of labeled medical image data. One of the data argumentation approach, Generative Adversarial Networks (GANs), can generate new samples that are drawn from the distribution of original dataset. Unfortunately, small sample medical imaging data is always not sufficient to train GANs with millions of parameters. Therefore, this paper proposes a pre-trained Style-based Generative Adversarial Networks (StyleGAN) to transfer knowledge from the Magnetic Resonance Imaging (MRI) domain to Computed tomography (CT) domain with limited sample images. This first pre-trained StyleGAN based Data augmentations (DA) method can generate high quality \(256\,\times \,256\) CT artifacts and artifact-free images for CT motion artifacts detection. Furthermore, we demonstrate this technique on a CT motion artifacts classification task and have achieved an improvement of \(5.59\%\) in sensitivity using synthetic images. Our results indicate that pre-trained models can provide priori knowledge to overcome the small sample problem in medical image processing.
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Su, K., Zhou, E., Sun, X., Wang, C., Yu, D., Luo, X. (2020). Pre-trained StyleGAN Based Data Augmentation for Small Sample Brain CT Motion Artifacts Detection. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_26
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DOI: https://doi.org/10.1007/978-3-030-65390-3_26
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