TransU²-Net: An Effective Medical Image Segmentation Framework Based on Transformer and U²-Net.

Li, Xiang; Fang, Xianjin; Yang, Gaoming; Su, Shuzhi; Zhu, Li; Yu, Zekuan

Li, Xiang; Fang, Xianjin; Yang, Gaoming; Su, Shuzhi; Zhu, Li; Yu, Zekuan.

Afiliação

Li X; School of Safety Science and EngineeringAnhui University of Science and Technology Huainan 232000 China.
Fang X; School of Computer Science and EngineeringAnhui University of Science and Technology Huainan 232000 China.
Yang G; Institute of Artificial IntelligenceHefei Comprehensive National Science Center Hefei 230009 China.
Su S; School of Computer Science and EngineeringAnhui University of Science and Technology Huainan 232000 China.
Zhu L; School of Computer Science and EngineeringAnhui University of Science and Technology Huainan 232000 China.
Yu Z; Shanghai Chest Hospital, School of MedicineShanghai Jiao Tong University Shanghai 200030 China.

IEEE J Transl Eng Health Med ; 11: 441-450, 2023.

Article em En | MEDLINE | ID: mdl-37817826

Assuntos

Neoplasias Encefálicas; Humanos; Neoplasias Encefálicas/diagnóstico por imagem; Relevância Clínica; Bases de Dados Factuais; Fontes de Energia Elétrica; Ética Médica

Palavras-chave

Deep learning; U-Net; medical image segmentation; transformer

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas Limite: Humans Idioma: En Revista: IEEE J Transl Eng Health Med Ano de publicação: 2023 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google