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Authors
Signaevsky, Maxim
Prastawa, Marcel
Farrell, Kurt
Tabish, Nabil
Baldwin, Elena
Han, Natalia
Iida, Megan A.
Koll, John
Bryce, Clare
Purohit, Dushyant
Haroutunian, Vahram
McKee, Ann C.
Stein, Thor D.
White, Charles L. III http://orcid.org/0000-0002-3870-2804
Walker, Jamie
Richardson, Timothy E.
Hanson, Russell
Donovan, Michael J.
Cordon-Cardo, Carlos
Zeineh, Jack
Fernandez, Gerardo
Crary, John F.
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Funding
Funding for this research was provided by:
U.S. Department of Health & Human Services | NIH | National Institute on Aging (F32AG056098, R01AG054008, R01AG062348, RF1AG060961)
U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (R01NS095252)
U.S. Department of Defense (W81XWH-13-MRPRA-CSRA)
Alzheimer's Association (NIRG-15-363188)
Tau Consortium
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More Information
This article is maintained by: Elsevier
Article Title: Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy
Journal Title: Laboratory Investigation
CrossRef DOI link to publisher maintained version: https://doi.org/10.1038/s41374-019-0202-4
Content Type: article
Copyright: © 2019 United States & Canadian Academy of Pathology.
