Interpretable Classification of Tauopathies with a Convolutional Neural Network Pipeline Using Transfer Learning and Validation against Post-Mortem Clinical Cases of Alzheimer’s Disease and Progressive Supranuclear Palsy
Fecha
2022-11-22Autor
Diaz-Gomez, Liliana
Gutierrez-Rodriguez, Andres E.
Martinez-Maldonado, Alejandra
Luna-Muñoz, Jose
Cantoral-Ceballos, Jose A.
Ontiveros-Torres, Miguel A.
Metadatos
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Neurodegenerative diseases, tauopathies, constitute a serious global health problem.
The etiology of these diseases is unclear and an increase in their incidence has been projected
in the next 30 years. Therefore, the study of the molecular mechanisms that might stop these
neurodegenerative processes is very relevant. Classification of neurodegenerative diseases using
Machine and Deep Learning algorithms has been widely studied for medical imaging such as
Magnetic Resonance Imaging. However, post-mortem immunofluorescence imaging studies of the
brains of patients have not yet been used for this purpose. These studies may represent a valuable
tool for monitoring aberrant chemical changes or pathological post-translational modifications of
the Tau polypeptide. We propose a Convolutional Neural Network pipeline for the classification
of Tau pathology of Alzheimer’s disease and Progressive Supranuclear Palsy by analyzing postmortem
immunofluorescence images with different Tau biomarkers performed with models generated
with the architecture ResNet-IFT using Transfer Learning. These models’ outputs were interpreted
with interpretability algorithms such as Guided Grad-CAM and Occlusion Analysis. To determine
the best classifier, four different architectures were tested. We demonstrated that our design was
able to classify diseases with an accuracy of 98.41% on average whilst providing an interpretation
concerning the proper classification involving different structural patterns in the immunoreactivity
of the Tau protein in NFTs present in the brains of patients with Progressive Supranuclear Palsy and
Alzheimer’s disease.
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