Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising

Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising Publisher Pubmed

Ataei S¹ ; Babyn P² ; Ahmadian A³ ; Alirezaie J¹

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1. Ryerson Univeristy, Department of Electrical and Computer Engineering, Toronto, M5B2K3, ON, Canada
2. University of Saskatoon, Department of Medical Imaging, Saskatoon, S7N0W8, SK, Canada
3. Tehran University of Medical Sciences, Tehran, Iran

Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS Published:2021

Abstract

CT machines can be tuned in order to reduce the radiation dose used for imaging, yet reducing the radiation dose results in noisy images which are not suitable in clinical practice. In order for low dose CT to be used effectively in practice this issue must be addressed. Generative Adversarial Networks (GAN) have been used widely in computer vision research and have proven themselves as a powerful tool for producing images with high perceptual quality. In this work we use a cascade of two neural networks, the first is a Generative Adversarial Network and the second is a Deep Convolutional Neural Network. The first network generates a denoised sample which is then fine-tuned by the second network via residue learning. We show that our cascaded method outperforms related works and more effectively reconstructs fine structural details in low contrast regions of the image. © 2021 IEEE.

Style	Citing Format
MLA	Ataei S, et al.. "Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising." Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS, vol. , no. , 2021, pp. 3053-3056.
APA	Ataei S, Babyn P, Ahmadian A, Alirezaie J (2021). Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS, (), 3053-3056.
Chicago	Ataei S, Babyn P, Ahmadian A, Alirezaie J. "Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising." Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS , no. (2021): 3053-3056.
Harvard	Ataei S et al. (2021) 'Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS, (), pp. 3053-3056.
Vancouver	Ataei S, Babyn P, Ahmadian A, Alirezaie J. Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS. 2021;():3053-3056.
BibTex	@article{ author = {Ataei S and Babyn P and Ahmadian A and Alirezaie J}, title = {Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising}, journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS}, volume = {}, number = {}, pages = {3053-3056}, year = {2021} }
RIS	TY - JOUR AU - Ataei S AU - Babyn P AU - Ahmadian A AU - Alirezaie J TI - Cascaded Learning With Generative Adversarial Networks for Low Dose Ct Denoising JO - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS VL - IS - SP - 3053 EP - 3056 PY - 2021 ER -

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