Learning spatiotemporal features of DSA using 3D CNN and BiConvGRU for ischemic moyamoya disease detection.

Hu, Tao; Lei, Yu; Su, Jiabin; Yang, Heng; Ni, Wei; Gao, Chao; Yu, Jinhua; Wang, Yuanyuan; Gu, Yuxiang

Hu, Tao; Lei, Yu; Su, Jiabin; Yang, Heng; Ni, Wei; Gao, Chao; Yu, Jinhua; Wang, Yuanyuan; Gu, Yuxiang.

Afiliación

Hu T; Department of Electronic Engineering, Fudan University, Shanghai, China.
Lei Y; Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Su J; Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Yang H; Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Ni W; Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Gao C; Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Yu J; Department of Electronic Engineering, Fudan University, Shanghai, China.
Wang Y; Department of Electronic Engineering, Fudan University, Shanghai, China.
Gu Y; Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.

Int J Neurosci ; 133(5): 512-522, 2023 May.

Article en En | MEDLINE | ID: mdl-34042552

Asunto(s)

Enfermedad de Moyamoya; Humanos; Enfermedad de Moyamoya/diagnóstico por imagen; Angiografía de Substracción Digital/métodos; Redes Neurales de la Computación; Hemorragia Cerebral

Palabras clave

); 2D convolutional neural network (2D CNN; 3D convolutional neural network (3D CNN; Moyamoya disease (MMD) detection; bidirectional convolutional Gated Recurrent Unit (BiConvGRU; features fusion

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Moyamoya Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Int J Neurosci Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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