Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images

Bibliographic Details
Main Author: Lapa, Paulo
Publication Date: 2019
Other Authors: Rundo, Leonardo, Gonçalves, Ivo, Castelli, Mauro
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/152620
Summary: Lapa, P., Rundo, L., Gonçalves, I., & Castelli, M. (2019). Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: A study with the semantic learning machine. In GECCO 2019 : Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 381-382). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3322035 --- This work was partially supported by projects UID/MULTI/00308/2019 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT - Portuguese Foundation for Science and Technology and Regional Operational Program of the Center Region (CENTRO2020) within project MAnAGER (POCI-01-0145-FEDER-028040). This work was also partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET).
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spelling Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance imagesA study with the semantic learning machineClassificationConvolutional Neural NetworksMultiparamet-ric Magnetic Resonance ImagingNeuroevolutionProstate cancer detectionSemantic Learning MachineArtificial IntelligenceTheoretical Computer ScienceSoftwareSDG 3 - Good Health and Well-beingLapa, P., Rundo, L., Gonçalves, I., & Castelli, M. (2019). Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: A study with the semantic learning machine. In GECCO 2019 : Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 381-382). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3322035 --- This work was partially supported by projects UID/MULTI/00308/2019 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT - Portuguese Foundation for Science and Technology and Regional Operational Program of the Center Region (CENTRO2020) within project MAnAGER (POCI-01-0145-FEDER-028040). This work was also partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET).Prostate cancer (PCa) is the most common oncological disease in Western men. Even though a significant effort has been carried out by the scientific community, accurate and reliable automated PCa detection methods are still a compelling issue. In this clinical scenario, high-resolution multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, also enabling quantitative studies. Recently, deep learning techniques have achieved outstanding results in prostate MRI analysis tasks, in particular with regard to image classification. This paper studies the feasibility of using the Semantic Learning Machine (SLM) neuroevolution algorithm to replace the fully-connected architecture commonly used in the last layers of Convolutional Neural Networks (CNNs). The experimental phase considered the PROSTATEx dataset composed of multispectral MRI sequences. The achieved results show that, on the same non-contrast-enhanced MRI series, SLM outperforms with statistical significance a state-of-the-art CNN trained with backpropagation. The SLM performance is achieved without pre-training the underlying CNN with backpropagation. Furthermore, on average the SLM training time is approximately 14 times faster than the backpropagation-based approach.ACM - Association for Computing MachineryNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNLapa, PauloRundo, LeonardoGonçalves, IvoCastelli, Mauro2023-05-11T22:03:37Z2019-07-132019-07-13T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion2application/pdfhttp://hdl.handle.net/10362/152620eng9781450367486PURE: 14789493https://doi.org/10.1145/3319619.3322035info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:11:20Zoai:run.unl.pt:10362/152620Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:41:33.346103Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
A study with the semantic learning machine
title Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
spellingShingle Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
Lapa, Paulo
Classification
Convolutional Neural Networks
Multiparamet-ric Magnetic Resonance Imaging
Neuroevolution
Prostate cancer detection
Semantic Learning Machine
Artificial Intelligence
Theoretical Computer Science
Software
SDG 3 - Good Health and Well-being
title_short Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
title_full Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
title_fullStr Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
title_full_unstemmed Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
title_sort Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
author Lapa, Paulo
author_facet Lapa, Paulo
Rundo, Leonardo
Gonçalves, Ivo
Castelli, Mauro
author_role author
author2 Rundo, Leonardo
Gonçalves, Ivo
Castelli, Mauro
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Lapa, Paulo
Rundo, Leonardo
Gonçalves, Ivo
Castelli, Mauro
dc.subject.por.fl_str_mv Classification
Convolutional Neural Networks
Multiparamet-ric Magnetic Resonance Imaging
Neuroevolution
Prostate cancer detection
Semantic Learning Machine
Artificial Intelligence
Theoretical Computer Science
Software
SDG 3 - Good Health and Well-being
topic Classification
Convolutional Neural Networks
Multiparamet-ric Magnetic Resonance Imaging
Neuroevolution
Prostate cancer detection
Semantic Learning Machine
Artificial Intelligence
Theoretical Computer Science
Software
SDG 3 - Good Health and Well-being
description Lapa, P., Rundo, L., Gonçalves, I., & Castelli, M. (2019). Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: A study with the semantic learning machine. In GECCO 2019 : Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 381-382). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3322035 --- This work was partially supported by projects UID/MULTI/00308/2019 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT - Portuguese Foundation for Science and Technology and Regional Operational Program of the Center Region (CENTRO2020) within project MAnAGER (POCI-01-0145-FEDER-028040). This work was also partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET).
publishDate 2019
dc.date.none.fl_str_mv 2019-07-13
2019-07-13T00:00:00Z
2023-05-11T22:03:37Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/152620
url http://hdl.handle.net/10362/152620
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9781450367486
PURE: 14789493
https://doi.org/10.1145/3319619.3322035
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 2
application/pdf
dc.publisher.none.fl_str_mv ACM - Association for Computing Machinery
publisher.none.fl_str_mv ACM - Association for Computing Machinery
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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