Nature Communications (May 2019)

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

  • Ziqi Tang,
  • Kangway V. Chuang,
  • Charles DeCarli,
  • Lee-Way Jin,
  • Laurel Beckett,
  • Michael J. Keiser,
  • Brittany N. Dugger

DOI
https://doi.org/10.1038/s41467-019-10212-1
Journal volume & issue
Vol. 10, no. 1
pp. 1 – 14

Abstract

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Convolutional neural networks have been applied to various areas of medical imaging and histology. Here the authors develop an automated approach using interpretable neural networks to determine Alzheimer’s disease plaque and cerebral amyloid angiopathy burden in post-mortem human brain tissue.