DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal University of Espírito Santo-
Autor(es): dc.contributorOstbayerische Technische Hochschule Regensburg (OTH Regensburg)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity Hospital Augsburg-
Autor(es): dc.creatorSouza Jr, Luis A.-
Autor(es): dc.creatorPacheco, André G. C.-
Autor(es): dc.creatorPassos, Leandro A.-
Autor(es): dc.creatorSantana, Marcos C. S.-
Autor(es): dc.creatorMendel, Robert-
Autor(es): dc.creatorEbigbo, Alanna-
Autor(es): dc.creatorProbst, Andreas-
Autor(es): dc.creatorMessmann, Helmut-
Autor(es): dc.creatorPalm, Christoph-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T16:28:52Z-
Data de disponibilização: dc.date.available2025-08-21T16:28:52Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00521-024-09615-z-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308009-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308009-
Descrição: dc.descriptionLimitations in computer-assisted diagnosis include lack of labeled data and inability to model the relation between what experts see and what computers learn. 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. While deep learning techniques are broad so that unseen information might help learn patterns of interest, human insights to describe objects of interest help in decision-making. This paper proposes a novel approach, DeepCraftFuse, to address the challenge of combining information provided by deep networks with visual-based features to significantly enhance the correct identification of cancerous tissues in patients affected with Barrett’s esophagus (BE). We demonstrate that DeepCraftFuse outperforms state-of-the-art techniques on private and public datasets, reaching results of around 95% when distinguishing patients affected by BE that is either positive or negative to esophageal cancer.-
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.descriptionDepartment of Informatics Federal University of Espírito Santo-
Descrição: dc.descriptionRegensburg Medical Image Computing (ReMIC) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
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-
Formato: dc.format10445-10459-
Idioma: dc.languageen-
Relação: dc.relationNeural Computing and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAdenocarcinoma-
Palavras-chave: dc.subjectBarrett’s esophagus-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectObject detector-
Título: dc.titleDeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.