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Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks

dc.contributor.authorde Souza, Luis A.
dc.contributor.authorPassos, Leandro A. [UNESP]
dc.contributor.authorMendel, Robert
dc.contributor.authorEbigbo, Alanna
dc.contributor.authorProbst, Andreas
dc.contributor.authorMessmann, Helmut
dc.contributor.authorPalm, Christoph
dc.contributor.authorPapa, João P. [UNESP]
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionOstbayerische Technische Hochschule Regensburg (OTH Regensburg)
dc.contributor.institutionUniversity Hospital Augsburg
dc.date.accessioned2021-06-25T11:05:20Z
dc.date.available2021-06-25T11:05:20Z
dc.date.issued2020-11-01
dc.description.abstractBarrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.en
dc.description.affiliationDepartment of Computing São Carlos Federal University UFSCar
dc.description.affiliationDepartment of Computing São Paulo State University UNESP
dc.description.affiliationRegensburg Medical Image Computing (ReMIC) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)
dc.description.affiliationRegensburg Center of Health Sciences and Technology (RCHST) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)
dc.description.affiliationDepartment of Gastroenterology University Hospital Augsburg
dc.description.affiliationUnespDepartment of Computing São Paulo State University UNESP
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipAlexander von Humboldt-Stiftung
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdFAPESP: 2017/04847-9
dc.description.sponsorshipIdFAPESP: 2019/06533-7
dc.description.sponsorshipIdFAPESP: 2019/07665-4
dc.description.sponsorshipIdFAPESP: 2019/08605-5
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.description.sponsorshipIdCNPq: 307066/2017-7
dc.description.sponsorshipIdAlexander von Humboldt-Stiftung: BEX 0581-16-0
dc.identifierhttp://dx.doi.org/10.1016/j.compbiomed.2020.104029
dc.identifier.citationComputers in Biology and Medicine, v. 126.
dc.identifier.doi10.1016/j.compbiomed.2020.104029
dc.identifier.issn1879-0534
dc.identifier.issn0010-4825
dc.identifier.scopus2-s2.0-85092441701
dc.identifier.urihttp://hdl.handle.net/11449/208039
dc.language.isoeng
dc.relation.ispartofComputers in Biology and Medicine
dc.sourceScopus
dc.subjectAdenocarcinoma
dc.subjectBarrett's esophagus
dc.subjectGenerative adversarial networks
dc.subjectMachine learning
dc.titleAssisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networksen
dc.typeArtigopt
dspace.entity.typePublication
relation.isDepartmentOfPublication872c0bbb-bf84-404e-9ca7-f87a0fe94e58
relation.isDepartmentOfPublication.latestForDiscovery872c0bbb-bf84-404e-9ca7-f87a0fe94e58
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unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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