CNN Ensembles for Nuclei Segmentation on Histological Images of OED

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorFederal Institute of Triângulo Mineiro-
Autor(es): dc.contributorUniversity of Bologna-
Autor(es): dc.creatorSilva, Adriano B.-
Autor(es): dc.creatorRozendo, Guilherme B.-
Autor(es): dc.creatorTosta, Thaina A. A.-
Autor(es): dc.creatorMartins, Alessandro S.-
Autor(es): dc.creatorLoyola, Adriano M.-
Autor(es): dc.creatorCardoso, Sergio V.-
Autor(es): dc.creatorLumini, Alessandra-
Autor(es): dc.creatorNeves, Leandro A.-
Autor(es): dc.creatorDe Faria, Paulo R.-
Autor(es): dc.creatorNascimento, Marcelo Z. Do-
Data de aceite: dc.date.accessioned2025-08-21T16:45:46Z-
Data de disponibilização: dc.date.available2025-08-21T16:45:46Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/CBMS58004.2023.00286-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/305641-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/305641-
Descrição: dc.descriptionEarly diagnosis of potentially malignant disorders, such as oral epithelial dysplasia (OED), is the most reliable way to prevent oral cancer. Computational algorithms have been used as a tool to aid specialists in this process. In recent years, CNN-based methods have gained more attention due to their improved results in nuclei segmentation tasks. Despite these relevant results, achieving high segmentation accuracy remains a challenging task. In this paper, we propose an ensemble of segmentation models to improve the performance of nuclei segmentation in OED histopathology images. The proposed ensemble consists of four CNN segmentation models, which were combined using three ensemble strategies: simple averaging, weighted averaging and majority voting, achieved accuracy of 90.69%, 90.70% and 88.49%, respectively, when applied to OED images. The model's performance was also evaluated on three publicly available datasets and achieved comparable performance to state-of-the-art segmentation methods. These values indicate that the proposed ensemble methods can be used in medical image analysis applications.-
Descrição: dc.descriptionFederal University of Uberlândia Faculty of Computer Science-
Descrição: dc.descriptionSão Paulo State University Department of Computer Science and Statistics (DCCE)-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo-
Descrição: dc.descriptionFederal Institute of Triângulo Mineiro-
Descrição: dc.descriptionSchool of Dentistry Federal University of Uberlândia Area of Oral Pathology-
Descrição: dc.descriptionUniversity of Bologna Department of Computer Science and Engineering (DISI)-
Descrição: dc.descriptionInstitute of Biomedical Science Federal University of Uberlândia Department of Histology and Morphology-
Descrição: dc.descriptionSão Paulo State University Department of Computer Science and Statistics (DCCE)-
Formato: dc.format601-604-
Idioma: dc.languageen-
Relação: dc.relationProceedings - IEEE Symposium on Computer-Based Medical Systems-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCNN Ensemble-
Palavras-chave: dc.subjectHistological Image Processing-
Palavras-chave: dc.subjectNuclei Segmentation-
Palavras-chave: dc.subjectOral Epihtelial Dysplasia-
Título: dc.titleCNN Ensembles for Nuclei Segmentation on Histological Images of OED-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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