Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Interpretable classification of proteinopathies with a convolutional neural network pipeline using transfer learning and validation against post-mortem clinical cases of alzheimer’s disease and progressive supranuclear palsy using biomarkers with tau polypeptide(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) Díaz Gómez, Liliana; CANTORAL CEBALLOS, JOSE ANTONIO; 261286; Cantoral Ceballos, José Antonio; puemcuervo, emipsanchez; Gutiérrez Rodríguez, Andres Eduardo; González Mendoza, Miguel; Sosa Hernández, Victor Adrián; Castañeda Miranda, Alejandro; School of Engineering and Sciences; Campus Monterrey; Ontiveros Torres, Miguel AngelNeurodegenerative 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 detonate 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 post-mortem immunofluorescence images with different Tau biomarkers performed by models generated with the architecture ResNet-IFT using Transfer Learning. These models’ outputs were interpreted by interpretability algorithms 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 of the proper classification involving different structural patterns in the immunoreactivity of the Tau protein in neurofibrillary tangles present in the brains of patients with Progressive Supranuclear Palsy and Alzheimer’s disease.