| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-020-19527-w |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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