Denoising digital breast tomosynthesis projections using convolutional neural networks

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorDe Araújo, Darlan M.N. [UNESP]-
Autor(es): dc.creatorSalvadeo, Denis H. P. [UNESP]-
Autor(es): dc.creatorDe Paula, Davi D. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:46:17Z-
Data de disponibilização: dc.date.available2022-02-22T00:46:17Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1117/12.2582185-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/206144-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/206144-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionSão Paulo State Univ. (Unesp) Institute of Geosciences and Exact Sciences (IGCE)-
Descrição: dc.descriptionSão Paulo State Univ. (Unesp) Institute of Geosciences and Exact Sciences (IGCE)-
Idioma: dc.languageen-
Relação: dc.relationProgress in Biomedical Optics and Imaging - Proceedings of SPIE-
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Palavras-chave: dc.subjectConvolutional neural networks-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectDenoising-
Palavras-chave: dc.subjectDigital breast tomosynthesis-
Título: dc.titleDenoising digital breast tomosynthesis projections using convolutional neural networks-
Aparece nas coleções:Repositório Institucional - Unesp

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