ArticleActa neuropathologica communications2022
Deep learning from multiple experts improves identification of amyloid neuropathologies.
Article in Acta neuropathologica communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease.EMBO molecular medicine · 2026Article
- Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.Scientific reports · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Deep learning finds convergent melanocytic morphology despite noisy archival slides.Cell reports methods · 2025Article
- Comparison of multiple quantitative strategies for neuropathologic image analyses.Journal of neuropathology and experimental neurology · 2025Article
- Development of a sperm morphology assessment standardization training tool.Biology methods & protocols · 2025Article
- Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.Acta neuropathologica communications · 2024Article
- Deep learning with noisy labels in medical prediction problems: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.Acta neuropathologica communications · 2023Article
- High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies.Journal of neuropathology and experimental neurology · 2023Article
- The neuropathological landscape of Hispanic and non-Hispanic White decedents with Alzheimer disease.Acta neuropathologica communications · 2023Article
- Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.Communications biology · 2023Article
- The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers.Journal of neuropathology and experimental neurology · 2023Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.bioRxiv : the preprint server for biology · 2023Article
- Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks.Free neuropathology · 2023Article
- Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm.Frontiers in artificial intelligence · 2023Review
- Artificial intelligence-derived neurofibrillary tangle burden is associated with antemortem cognitive impairment.Acta neuropathologica communications · 2022Article
- Artificial intelligence policies in neurology journals: a cross-sectional analysis.Frontiers in neurologyArticle
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13 authors.
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Abstract
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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