High-resolution generative adversarial neural networks applied to histological images generation
Descripción del Articulo
The present work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU), the Office of Research of Universidad Nacional de Ingeniería (VRI - UNI) and the research management office (OGI...
| Autores: | , , , |
|---|---|
| Formato: | objeto de conferencia |
| Fecha de Publicación: | 2018 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/634 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/634 https://doi.org/10.1007/978-3-030-01421-6_20 |
| Nivel de acceso: | acceso abierto |
| Materia: | Statistical correlation Deep learning Diagnosis Medical imaging Neural networks Diagnostic algorithms Generative Adversarial Nets High resolution Histological images Learning-based methods Photo realistic image synthesis Photorealistic images Image analysis https://purl.org/pe-repo/ocde/ford#2.02.04 |
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| dc.title.none.fl_str_mv |
High-resolution generative adversarial neural networks applied to histological images generation |
| title |
High-resolution generative adversarial neural networks applied to histological images generation |
| spellingShingle |
High-resolution generative adversarial neural networks applied to histological images generation Mauricio A. Statistical correlation Deep learning Diagnosis Medical imaging Medical imaging Neural networks Diagnostic algorithms Generative Adversarial Nets High resolution Histological images Learning-based methods Photo realistic image synthesis Photorealistic images Image analysis https://purl.org/pe-repo/ocde/ford#2.02.04 |
| title_short |
High-resolution generative adversarial neural networks applied to histological images generation |
| title_full |
High-resolution generative adversarial neural networks applied to histological images generation |
| title_fullStr |
High-resolution generative adversarial neural networks applied to histological images generation |
| title_full_unstemmed |
High-resolution generative adversarial neural networks applied to histological images generation |
| title_sort |
High-resolution generative adversarial neural networks applied to histological images generation |
| author |
Mauricio A. |
| author_facet |
Mauricio A. López J. Huauya R. Diaz J. |
| author_role |
author |
| author2 |
López J. Huauya R. Diaz J. |
| author2_role |
author author author |
| dc.contributor.author.fl_str_mv |
Mauricio A. López J. Huauya R. Diaz J. |
| dc.subject.none.fl_str_mv |
Statistical correlation |
| topic |
Statistical correlation Deep learning Diagnosis Medical imaging Medical imaging Neural networks Diagnostic algorithms Generative Adversarial Nets High resolution Histological images Learning-based methods Photo realistic image synthesis Photorealistic images Image analysis https://purl.org/pe-repo/ocde/ford#2.02.04 |
| dc.subject.es_PE.fl_str_mv |
Deep learning Diagnosis Medical imaging Medical imaging Neural networks Diagnostic algorithms Generative Adversarial Nets High resolution Histological images Learning-based methods Photo realistic image synthesis Photorealistic images Image analysis |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#2.02.04 |
| description |
The present work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU), the Office of Research of Universidad Nacional de Ingeniería (VRI - UNI) and the research management office (OGI - UNI). |
| publishDate |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2018 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
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conferenceObject |
| dc.identifier.isbn.none.fl_str_mv |
9783030014209 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/634 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1007/978-3-030-01421-6_20 |
| dc.identifier.scopus.none.fl_str_mv |
2-s2.0-85054798854 |
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9783030014209 2-s2.0-85054798854 |
| url |
https://hdl.handle.net/20.500.12390/634 https://doi.org/10.1007/978-3-030-01421-6_20 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Springer Verlag |
| publisher.none.fl_str_mv |
Springer Verlag |
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reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
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Consejo Nacional de Ciencia Tecnología e Innovación |
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CONCYTEC |
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CONCYTEC |
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CONCYTEC-Institucional |
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CONCYTEC-Institucional |
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Repositorio Institucional CONCYTEC |
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repositorio@concytec.gob.pe |
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1844883106246426624 |
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Publicationrp00530500rp00914500rp01293600rp00946500Mauricio A.López J.Huauya R.Diaz J.2024-05-30T23:13:38Z2024-05-30T23:13:38Z20189783030014209https://hdl.handle.net/20.500.12390/634https://doi.org/10.1007/978-3-030-01421-6_202-s2.0-85054798854The present work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU), the Office of Research of Universidad Nacional de Ingeniería (VRI - UNI) and the research management office (OGI - UNI).For many years, synthesizing photo-realistic images has been a highly relevant task due to its multiple applications from aesthetic or artistic [19] to medical purposes [1, 6, 21]. Related to the medical area, this application has had greater impact because most classification or diagnostic algorithms require a significant amount of highly specialized images for their training yet obtaining them is not easy at all. To solve this problem, many works analyze and interpret images of a specific topic in order to obtain a statistical correlation between the variables that define it. By this way, any set of variables close to the map generated in the previous analysis represents a similar image. Deep learning based methods have allowed the automatic extraction of feature maps which has helped in the design of more robust models photo-realistic image synthesis. This work focuses on obtaining the best feature maps for automatic generation of synthetic histological images. To do so, we propose a Generative Adversarial Networks (GANs) [8] to generate the new sample distribution using the feature maps obtained by an autoencoder [14, 20] as latent space instead of a completely random one. To corroborate our results, we present the generated images against the real ones and their respective results using different types of autoencoder to obtain the feature maps.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengSpringer VerlagLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccessStatistical correlationDeep learning-1Diagnosis-1Medical imaging-1Medical imaging-1Neural networks-1Diagnostic algorithms-1Generative Adversarial Nets-1High resolution-1Histological images-1Learning-based methods-1Photo realistic image synthesis-1Photorealistic images-1Image analysis-1https://purl.org/pe-repo/ocde/ford#2.02.04-1High-resolution generative adversarial neural networks applied to histological images generationinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/634oai:repositorio.concytec.gob.pe:20.500.12390/6342024-05-30 15:35:52.031http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="5931ab74-9857-4bfb-a055-a04514f02131"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>High-resolution generative adversarial neural networks applied to histological images generation</Title> <PublishedIn> <Publication> <Title>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</Title> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <DOI>https://doi.org/10.1007/978-3-030-01421-6_20</DOI> <SCP-Number>2-s2.0-85054798854</SCP-Number> <ISBN>9783030014209</ISBN> <Authors> <Author> <DisplayName>Mauricio A.</DisplayName> <Person id="rp00530" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>López J.</DisplayName> <Person id="rp00914" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Huauya R.</DisplayName> <Person id="rp01293" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Diaz J.</DisplayName> <Person id="rp00946" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Springer Verlag</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Statistical correlation</Keyword> <Keyword>Deep learning</Keyword> <Keyword>Diagnosis</Keyword> <Keyword>Medical imaging</Keyword> <Keyword>Medical imaging</Keyword> <Keyword>Neural networks</Keyword> <Keyword>Diagnostic algorithms</Keyword> <Keyword>Generative Adversarial Nets</Keyword> <Keyword>High resolution</Keyword> <Keyword>Histological images</Keyword> <Keyword>Learning-based methods</Keyword> <Keyword>Photo realistic image synthesis</Keyword> <Keyword>Photorealistic images</Keyword> <Keyword>Image analysis</Keyword> <Abstract>For many years, synthesizing photo-realistic images has been a highly relevant task due to its multiple applications from aesthetic or artistic [19] to medical purposes [1, 6, 21]. Related to the medical area, this application has had greater impact because most classification or diagnostic algorithms require a significant amount of highly specialized images for their training yet obtaining them is not easy at all. To solve this problem, many works analyze and interpret images of a specific topic in order to obtain a statistical correlation between the variables that define it. By this way, any set of variables close to the map generated in the previous analysis represents a similar image. Deep learning based methods have allowed the automatic extraction of feature maps which has helped in the design of more robust models photo-realistic image synthesis. This work focuses on obtaining the best feature maps for automatic generation of synthetic histological images. To do so, we propose a Generative Adversarial Networks (GANs) [8] to generate the new sample distribution using the feature maps obtained by an autoencoder [14, 20] as latent space instead of a completely random one. To corroborate our results, we present the generated images against the real ones and their respective results using different types of autoencoder to obtain the feature maps.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
| score |
13.394457 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
