14 citations · 31 across the 6 of their papers we have counts for
6 papers
Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies
Di Wang, Nicolas Honnorat, Peter T. Fox +4
Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and h…
Variational AutoEncoder For Regression: Application to Brain Aging Analysis
Qingyu Zhao, Ehsan Adeli, Nicolas Honnorat +2
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close th…
Truncated Gaussian-Mixture Variational AutoEncoder
Qingyu Zhao, Nicolas Honnorat, Ehsan Adeli +1
Variation Autoencoder (VAE) has become a powerful tool in modeling the non-linear generative process of data from a low-dimensional latent space. Recently, several studies have pro…
Sparse Hierachical Extrapolated Parametric Methods for Cortical Data Analysis
Nicolas Honnorat, Christos Davatzikos
Many neuroimaging studies focus on the cortex, in order to benefit from better signal to noise ratios and reduced computational burden. Cortical data are usually projected onto a r…
Riccati-regularized Precision Matrices for Neuroimaging
Nicolas Honnorat, Christos Davatzikos
The introduction of graph theory in neuroimaging has pro- vided invaluable tools for the study of brain connectivity. These methods require the definition of a graph, which is typi…
Nuquantus: Machine learning software for the characterization and quantification of cell nuclei in complex immunofluorescent tissue images
Polina Gross, Nicolas Honnorat, Erdem Varol +8
Determination of fundamental mechanisms of disease often hinges on histopathology visualization and quantitative image analysis. Currently, the analysis of multi-channel fluorescen…