Citations 3
Diagnosis of Alzheimer’s Disease and Tauopathies on Whole Slide Images using Deep Learning Algorithm (S39.001)
Castanedes‐Casey, Monica , Yao, Gary, Hwang, Tae Hyun , Koga, Shunsuke , Kim, Minji , Sekiya, Hiroaki , Martin, Nicholas G. , Dickson, Dennis W.
Neurology | April 28, 2023 | Volume: 100 | Issue: 17_supplement_2 | Pages: No page available
Diagnosis of Alzheimer’s disease and tauopathies on whole slide histopathology images using a weakly supervised deep learning algorithm
Yao, Gary, Martin, Nicholas B. , Castanedes‐Casey, Monica , Hwang, Tae Hyun , Koga, Shunsuke , Koga, Shunsuke , Kim, Minji , Sekiya, Hiroaki , Dickson, Dennis W.
No container title available | January 16, 2023 | Volume: No volume available | Issue: No issue available | Pages: No page available
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.
Diagnosis of Alzheimer’s disease and tauopathies on whole slide histopathology images using a weakly supervised deep learning algorithm
Martin, Nicholas B. , Castanedes‐Casey, Monica , Hwang, Tae Hyun , Koga, Shunsuke , Koga, Shunsuke , Kim, Minji , Sekiya, Hiroaki , Dickson, Dennis W.
No container title available | January 10, 2023 | Volume: No volume available | Issue: No issue available | Pages: No page available
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.