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.
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 identifying 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 annotated >70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieved 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 visualized morphology distributions for WSIs at high resolution. Resulting plaque-burden scores correlated well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrated that networks learned patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability may suggest a route to neuropathologic deep phenotyping. KeywordsMachine learning, Alzheimer's disease, amyloid, deep learning, deep phenotyping, interpretability, explainable AI, convolutional neural networks, dementia, immunohistochemistry, plaques, pathology.Extracellular deposition of amyloid beta (Aβ) plaques is a pathological hallmark of Alzheimer's disease (AD) 1,2 , a common neurodegenerative disease. Amyloid plaques have a diverse range of morphologies and neuroanatomic distributions 1 . The current consensus criteria for a neuropathological diagnosis of AD [3][4][5] incorporate protocols assessing plaque density and distribution; some researchers have hypothesized that plaques may be an initiating event in AD 5,6 . More precise measures of plaque morphologies (such as cored, neuritic, and diffuse) can serve as a basis for understanding disease progression and pathophysiology, providing guidance and insight into disease mechanisms 2,7-10 .For neuropathologic diagnosis, established semi-quantitative scales are used to assess plaque burden ( Fig. 1a ) 4,8,11,12 . The standard semi-quantitative criteria put forth by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) based on the manual assessment of the highest density of neocortical neuritic plaques 4,13 . Diffuse plaques, which may be the initial morphological type of Aβ 14,15 , can account for over 50% of plaque burden in preclinical cases but are not included in CERAD 16 . Furthermore, data on anatomical location ( i.e. Thal amyloid phase) are based on the presence of plaques regardless of type or density 5 . The potential for neuropathologic deep phenotyping efforts that account for anatomic location, diverse sources of proteinopathy, and quantitative pathology densities motivates the development of effective and scalable quantitative methods to differentiate pathological subty...
Pathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address this problem, we present a deep learning (DL) approach that draws on a cohort of experts, weighs each contribution, and is robust to noisy labels. We collected 100,495 annotations on 20,099 candidate amyloid beta neuropathologies (cerebral amyloid angiopathy (CAA), and cored and diffuse plaques) from three institutions, independently annotated by five experts. DL methods trained on a consensus-of-two strategy yielded 12.6–26% improvements by area under the precision recall curve (AUPRC) when compared to those that learned individualized annotations. This strategy surpassed individual-expert models, even when unfairly assessed on benchmarks favoring them. Moreover, ensembling over individual models was robust to hidden random annotators. In blind prospective tests of 52,555 subsequent expert-annotated images, the models labeled pathologies like their human counterparts (consensus model AUPRC = 0.74 cored; 0.69 CAA). This study demonstrates a means to combine multiple ground truths into a common-ground DL model that yields consistent diagnoses informed by multiple and potentially variable expert opinions.
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