Layer-selective deep representation to improve esophageal cancer classification

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorEspírito Santo Federal University-
Autor(es): dc.contributorUniversity of Wolverhampton-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorOstbayerische Technische Hochschule Regensburg (OTH Regensburg)-
Autor(es): dc.contributorUniversity Hospital Augsburg-
Autor(es): dc.creatorSouza, Luis A.-
Autor(es): dc.creatorPassos, Leandro A.-
Autor(es): dc.creatorSantana, Marcos Cleison S.-
Autor(es): dc.creatorMendel, Robert-
Autor(es): dc.creatorRauber, David-
Autor(es): dc.creatorEbigbo, Alanna-
Autor(es): dc.creatorProbst, Andreas-
Autor(es): dc.creatorMessmann, Helmut-
Autor(es): dc.creatorPapa, João Paulo-
Autor(es): dc.creatorPalm, Christoph-
Data de aceite: dc.date.accessioned2025-08-21T18:17:17Z-
Data de disponibilização: dc.date.available2025-08-21T18:17:17Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s11517-024-03142-8-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309738-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309738-
Descrição: dc.descriptionAbstract: Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. For this task, the deep learning techniques’ black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett’s esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant improvement when the most discriminative layers expressed more impact in the training and classification of ResNet-50 for Barrett’s esophagus and adenocarcinoma classification, demonstrating that both human knowledge and computational processing may influence the correct learning of such a problem. Graphical abstract: (Figure presented.)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionAlexander von Humboldt-Stiftung-
Descrição: dc.descriptionEngineering and Physical Sciences Research Council-
Descrição: dc.descriptionDepartment of Informatics Espírito Santo Federal University-
Descrição: dc.descriptionCMI Lab School of Engineering and Informatics University of Wolverhampton-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionRegensburg Medical Image Computing (ReMIC) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)-
Descrição: dc.descriptionDepartment of Gastroenterology University Hospital Augsburg-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionFAPESP: 2013/07375-0-
Descrição: dc.descriptionFAPESP: 2014/12236- 1-
Descrição: dc.descriptionFAPESP: 2016/19403-6-
Descrição: dc.descriptionFAPESP: 2017/04847-9-
Descrição: dc.descriptionFAPESP: 2019/08605-5-
Descrição: dc.descriptionCNPq: 306166/2014-3-
Descrição: dc.descriptionCNPq: 307066/2017-7-
Descrição: dc.descriptionAlexander von Humboldt-Stiftung: BEX 0581-16-0-
Descrição: dc.descriptionEngineering and Physical Sciences Research Council: EP/T021063/1-
Formato: dc.format3355-3372-
Idioma: dc.languageen-
Relação: dc.relationMedical and Biological Engineering and Computing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBarrett’s esophagus detection-
Palavras-chave: dc.subjectConvolutional neural networks-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectMultistep training-
Título: dc.titleLayer-selective deep representation to improve esophageal cancer classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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