Self-calibrated convolution towards glioma segmentation

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorComputational Photography Department (DFC)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSalvagnini, Felipe C. R.-
Autor(es): dc.creatorBarbosa, Gerson O.-
Autor(es): dc.creatorFalcao, Alexandre X.-
Autor(es): dc.creatorSantos, Cid A. N.-
Data de aceite: dc.date.accessioned2025-08-21T16:04:30Z-
Data de disponibilização: dc.date.available2025-08-21T16:04:30Z-
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/SIPAIM56729.2023.10373517-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308428-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308428-
Descrição: dc.descriptionAccurate brain tumor segmentation in the early stages of the disease is crucial for the treatment's effectiveness, avoiding exhaustive visual inspection of a qualified specialist on 3D MR brain images of multiple protocols (e.g., T1, T2, T2-FLAIR, T1-Gd). Several networks exist for Glioma segmentation, being nnU-Net one of the best. In this work, we evaluate self-calibrated convolutions in different parts of the nnU-Net network to demonstrate that self-calibrated modules in skip connections can significantly improve the enhanced-tumor and tumor-core segmentation accuracy while preserving the wholetumor segmentation accuracy.-
Descrição: dc.descriptionEldorado Institute Computational Photography Department (DFC)-
Descrição: dc.descriptionState University of Campinas (UNICAMP)-
Descrição: dc.descriptionSão Paulo State University (UNESP)-
Descrição: dc.descriptionSão Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the 19th International Symposium on Medical Information Processing and Analysis, SIPAIM 2023-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subject3D Image Segmentation-
Palavras-chave: dc.subjectMedical Image Analysis-
Palavras-chave: dc.subjectNeural Networks-
Título: dc.titleSelf-calibrated convolution towards glioma segmentation-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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