A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

Interpretation of Computed Tomography Angiography for intracranial aneurysm diagnosis can be time-consuming and challenging. Here, the authors present a deep-learning-based framework achieving improved performance compared to that of radiologists and expert neurosurgeons.

Guardado en:
Detalles Bibliográficos
Autores principales: Zhao Shi, Chongchang Miao, U. Joseph Schoepf, Rock H. Savage, Danielle M. Dargis, Chengwei Pan, Xue Chai, Xiu Li Li, Shuang Xia, Xin Zhang, Yan Gu, Yonggang Zhang, Bin Hu, Wenda Xu, Changsheng Zhou, Song Luo, Hao Wang, Li Mao, Kongming Liang, Lili Wen, Longjiang Zhou, Yizhou Yu, Guang Ming Lu, Long Jiang Zhang
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
Materias:
Science
Q
Acceso en línea:https://doaj.org/article/5ecb0208268e4d118fe4dd3dd8db1b14
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

Internet

https://doaj.org/article/5ecb0208268e4d118fe4dd3dd8db1b14

Ejemplares similares