1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPEW34M/45EHMTL |
Repository | sid.inpe.br/sibgrapi/2021/09.16.16.33 |
Last Update | 2021:09.30.12.39.29 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2021/09.16.16.33.49 |
Metadata Last Update | 2022:09.10.00.16.17 (UTC) administrator |
Citation Key | PaulaSalv:2021:BrToRe |
Title | Breast Tomosynthesis Reconstruction Using Artificial Neural Networks with Deep Learning |
Format | On-line |
Year | 2021 |
Access Date | 2024, May 19 |
Number of Files | 1 |
Size | 2529 KiB |
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2. Context | |
Author | 1 Paula, Davi Duarte de 2 Salvadeo, Denis Henrique Pinheiro |
Affiliation | 1 São Paulo State University (Unesp) - Institute of Geosciences and Exact Sciences 2 São Paulo State University (Unesp) - Institute of Geosciences and Exact Sciences |
Editor | Paiva, Afonso Menotti, David Baranoski, Gladimir V. G. Proença, Hugo Pedro Junior, Antonio Lopes Apolinario Papa, João Paulo Pagliosa, Paulo dos Santos, Thiago Oliveira e Sá, Asla Medeiros da Silveira, Thiago Lopes Trugillo Brazil, Emilio Vital Ponti, Moacir A. Fernandes, Leandro A. F. Avila, Sandra |
e-Mail Address | davi.duarte@unesp.br |
Conference Name | Conference on Graphics, Patterns and Images, 34 (SIBGRAPI) |
Conference Location | Gramado, RS, Brazil (virtual) |
Date | 18-22 Oct. 2021 |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Book Title | Proceedings |
Tertiary Type | Master's or Doctoral Work |
History (UTC) | 2021-09-30 12:39:30 :: davi.duarte@unesp.br -> administrator :: 2021 2022-09-10 00:16:17 :: administrator -> :: 2021 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Keywords | Deep Learning Breast Tomosynthesis Image Reconstruction |
Abstract | The Filtered Backprojection (FBP) algorithm for Computed Tomography (CT) reconstruction can be mapped entire in an Artificial Neural Network (ANN), with the backprojection (BP) operation simulated analytically in a layer and the Ram-Lak filter simulated as a convolutional layer. Thus, this work adapt the BP layer for DBT reconstruction, making possible the use of FBP simulated as a ANN to reconstruct DBT images. For evaluation, Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics were calculated to measure the improvement of the images made by the ANN, regarding a dataset containing 100 virtual breast phantoms to perform the experiments. We shown that making the Ram-Lak layer trainable, the reconstructed image can be improved in terms of noise reduction. And, considering an additional post-filtering step performed by Denoising Convolutional Neural Network (DnCNN), it shown comparable and superior results than a state-of-the-art DBT reconstruction method, averaging 37.644 dB and 0.869 values of PSNR and SSIM, respectively. Finally, this study enables additional proposals of ANN with Deep Learning models for DBT reconstruction and denoising. |
Arrangement | urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Breast Tomosynthesis Reconstruction... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/45EHMTL |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/45EHMTL |
Language | en |
Target File | artigo_final.pdf |
User Group | davi.duarte@unesp.br |
Visibility | shown |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/45PQ3RS |
Citing Item List | sid.inpe.br/sibgrapi/2021/11.12.11.46 6 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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