Deep Learning–Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model

IMPORTANCE: Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic. OBJECTIVE: To develop and apply a neural network segmentation model (the HeadXNet...

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Detalles Bibliográficos
Autores principales: Park, Allison, Chute, Chris, Rajpurkar, Pranav, Lou, Joe, Ball, Robyn L., Shpanskaya, Katie, Jabarkheel, Rashad, Kim, Lily H., McKenna, Emily, Tseng, Joe, Ni, Jason, Wishah, Fidaa, Wittber, Fred, Hong, David S., Wilson, Thomas J., Halabi, Safwan, Basu, Sanjay, Patel, Bhavik N., Lungren, Matthew P., Ng, Andrew Y., Yeom, Kristen W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2019
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563570/
https://www.ncbi.nlm.nih.gov/pubmed/31173130
http://dx.doi.org/10.1001/jamanetworkopen.2019.5600

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563570/
https://www.ncbi.nlm.nih.gov/pubmed/31173130
http://dx.doi.org/10.1001/jamanetworkopen.2019.5600

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