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15 February 2021 Denoising digital breast tomosynthesis projections using convolutional neural networks
Darlan M. N. de Araújo, Denis H. P. Salvadeo, Davi D. de Paula
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Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115961L (2021) https://doi.org/10.1117/12.2582185
Event: SPIE Medical Imaging, 2021, Online Only
Abstract
The Digital Breast Tomosynthesis (DBT) projections are obtained with low quality, being essential to use denoising methods to increase the quality of the projections. Currently, deep learning methods have become the state-of-art approach in denoising. Some papers have proposed to apply deep learning methods for denoising DBT projections, however, there is a lack of clarity in the results comparing with traditional methods. In this paper, we proposed to use a CNN to denoise DBT projections, and compare it with traditional denoising methods. The results shown that the CNN is superior quantitatively and qualitatively in comparison with the traditional methods.
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Darlan M. N. de Araújo, Denis H. P. Salvadeo, and Davi D. de Paula "Denoising digital breast tomosynthesis projections using convolutional neural networks", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115961L (15 February 2021); https://doi.org/10.1117/12.2582185
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KEYWORDS
Digital breast tomosynthesis
Denoising
Convolutional neural networks
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