ArticleCommunications biology2023
Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Amyloid-β plaque diversity across brain regions in Alzheimer's disease and its relation to major clinicopathological traits.Acta neuropathologica · 2026Article
- Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.Scientific reports · 2026Article
- Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.Journal of neuropathology and experimental neurology · 2026Article
- Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.Journal of neuropathology and experimental neurology · 2025Article
- Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.Acta neuropathologica communications · 2023Article
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathy (CAA). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that approximately correlated with gold-standard human CERAD-like WSI scoring (p = 0.07 ± 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.
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Registered trials
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