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 () and Brittany N. Dugger ()
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Ziqi Tang: University of California, San Francisco
Kangway V. Chuang: University of California, San Francisco
Charles DeCarli: University of California-Davis School of Medicine
Lee-Way Jin: University of California-Davis School of Medicine
Laurel Beckett: University of California-Davis, Medical Science
Michael J. Keiser: University of California, San Francisco
Brittany N. Dugger: University of California-Davis School of Medicine

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: Abstract Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies—amyloid plaques and cerebral amyloid angiopathy—in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist’s ability suggests a route to neuropathologic deep phenotyping.

Date: 2019
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DOI: 10.1038/s41467-019-10212-1

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