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- W4316505874 abstract "Abstract Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies." @default.
- W4316505874 created "2023-01-16" @default.
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- W4316505874 date "2023-01-16" @default.
- W4316505874 modified "2023-10-18" @default.
- W4316505874 title "Diagnosis of Alzheimer’s disease and tauopathies on whole slide histopathology images using a weakly supervised deep learning algorithm" @default.
- W4316505874 doi "https://doi.org/10.21203/rs.3.rs-2459626/v2" @default.
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