Predicting four-year's Alzheimer's disease onset using longitudinal neurocognitive tests and MRI data using explainable deep convolutional neural networks
… approaches which use extracted regional brain volumes or thicknesses, CNN used whole-brain
MRI data without the need to extract regional brain volumes or thicknesses a priori [10–…
MRI data without the need to extract regional brain volumes or thicknesses a priori [10–…
MRI-Based Deep Learning Classification
FA Nidha, SS Jennifer, AW Reza - … of the 7th International Conference on … - books.google.com
… then altered their design to categorize 3D brain MRI data. The suggested ensemble model
showed … The author of this study [12] presented modal fusion, multimodal 3DSiameseNet, 3D …
showed … The author of this study [12] presented modal fusion, multimodal 3DSiameseNet, 3D …
MRI-Based Deep Learning Classification of Alzheimer's and Parkinson's Disease
… their design to categorize 3D brain MRI data. The suggested … PD based on 3D T1weighted
brain MRI and a random forest … modal fusion, multimodal 3DSiameseNet, 3D convolutional …
brain MRI and a random forest … modal fusion, multimodal 3DSiameseNet, 3D convolutional …
Analyse des pathologies neuro-dégénératives par apprentissage profond
C Ostertag - 2022 - theses.hal.science
… both structural brain MRI images and clinical data. In this thesis we propose an architecture
made of sub-modules tailored to each modality: 3D convolutional network for the brain MRI, …
made of sub-modules tailored to each modality: 3D convolutional network for the brain MRI, …