Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorOstbayerische Technische Hochschule Regensburg (OTH Regensburg)-
Autor(es): dc.contributorUniversitätsklinikum Augsburg-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorRegensburg Center of Health Sciences and Technology (RCHST)-
Autor(es): dc.creatorde Souza, Luis A.-
Autor(es): dc.creatorMendel, Robert-
Autor(es): dc.creatorStrasser, Sophia-
Autor(es): dc.creatorEbigbo, Alanna-
Autor(es): dc.creatorProbst, Andreas-
Autor(es): dc.creatorMessmann, Helmut-
Autor(es): dc.creatorPapa, João P.-
Autor(es): dc.creatorPalm, Christoph-
Data de aceite: dc.date.accessioned2025-08-21T16:50:25Z-
Data de disponibilização: dc.date.available2025-08-21T16:50:25Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2021-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.compbiomed.2021.104578-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233177-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233177-
Descrição: dc.descriptionEven though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of early-cancerous tissues in Barrett's esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts' delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.-
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.descriptionDepartment of Computing São Carlos Federal University - UFSCar-
Descrição: dc.descriptionRegensburg Medical Image Computing (ReMIC) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)-
Descrição: dc.descriptionMedizinische Klinik III Universitätsklinikum Augsburg-
Descrição: dc.descriptionDepartment of Computing São Paulo State University UNESP-
Descrição: dc.descriptionRegensburg Center of Health Sciences and Technology (RCHST)-
Descrição: dc.descriptionDepartment of Computing São Paulo State University UNESP-
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-
Idioma: dc.languageen-
Relação: dc.relationComputers in Biology and Medicine-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAdenocarcinoma-
Palavras-chave: dc.subjectBarrett's esophagus-
Palavras-chave: dc.subjectComputer-aided diagnosis-
Palavras-chave: dc.subjectExplainable artificial intelligence-
Palavras-chave: dc.subjectMachine learning-
Título: dc.titleConvolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.