PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging

PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging

Abstract Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and immediate treatment are critical to avoid high morbidity and mortality rates, yet PE remains among the diagnoses most...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Shih-Cheng Huang, Tanay Kothari, Imon Banerjee, Chris Chute, Robyn L. Ball, Norah Borus, Andrew Huang, Bhavik N. Patel, Pranav Rajpurkar, Jeremy Irvin, Jared Dunnmon, Joseph Bledsoe, Katie Shpanskaya, Abhay Dhaliwal, Roham Zamanian, Andrew Y. Ng, Matthew P. Lungren
Format: article
Langue:EN
Publié: Nature Portfolio 2020
Sujets:
Computer applications to medicine. Medical informatics
R858-859.7
Accès en ligne:https://doaj.org/article/8ceb4d87e32c41efa595cc53fbb017ec
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!

Internet

https://doaj.org/article/8ceb4d87e32c41efa595cc53fbb017ec

Documents similaires