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

Journal article

Shih-Cheng Huang, Tanay Kothari, Imon Banerjee, Chris Chute, Robyn L Ball, Norah Borus, Andrew Huang, Bhavik N Patel, Pranav Rajpurkar, Jeremy Irvin, others

npj Digital Medicine, Nature Publishing Group, 2020, pp. 1–9

Cite

APA

Huang, S.-C., Kothari, T., Banerjee, I., Chute, C., Ball, R. L., Borus, N., … others. (2020). PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. Npj Digital Medicine, 3, 1–9.

Chicago/Turabian

Huang, Shih-Cheng, Tanay Kothari, Imon Banerjee, Chris Chute, Robyn L Ball, Norah Borus, Andrew Huang, et al. “PENet—a Scalable Deep-Learning Model for Automated Diagnosis of Pulmonary Embolism Using Volumetric CT Imaging.” npj Digital Medicine 3 (2020): 1–9.

MLA

Huang, Shih-Cheng, et al. “PENet—a Scalable Deep-Learning Model for Automated Diagnosis of Pulmonary Embolism Using Volumetric CT Imaging.” Npj Digital Medicine, vol. 3, Nature Publishing Group, 2020, pp. 1–9.