AIMC Topic: Plaque, Amyloid

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Deep Learning-Based Image Classification in Differentiating Tufted Astrocytes, Astrocytic Plaques, and Neuritic Plaques.

Journal of neuropathology and experimental neurology Mar 22, 2021
This study aimed to develop a deep learning-based image classification model that can differentiate tufted astrocytes (TA), astrocytic plaques (AP), and neuritic plaques (NP) based on images of tissue sections stained with phospho-tau immunohistochem...
Humans Deep Learning Astrocytes Proof of Concept Study Plaque, Amyloid
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Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia.

Journal of Alzheimer's disease : JAD Jan 1, 2021
BACKGROUND: Detailed pathology analysis and morphological quantification is tedious and prone to errors. Automatic image analysis can help to increase objectivity and reduce time. Here, we present the evaluation of the DeePathology STUDIO™ for automa...
Machine Learning Animals Image Processing, Computer-Assisted Mice Disease Models, Animal Microglia Alzheimer Disease Mice, Transgenic Plaque, Amyloid
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Robust supervised segmentation of neuropathology whole-slide microscopy images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jan 1, 2015
Alzheimer's disease is characterized by brain pathological aggregates such as Aβ plaques and neurofibrillary tangles which trigger neuroinflammation and participate to neuronal loss. Quantification of these pathological markers on histological sectio...
Humans Brain Animals Image Processing, Computer-Assisted Mice Color Supervised Machine Learning Alzheimer Disease Microscopy Signal-To-Noise Ratio Neurofibrillary Tangles Plaque, Amyloid
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