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Multimodal classification of Alzheimer's disease and mild cognitive impairment
Three modalities of biomarkers are proposed to combine, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method, and shows considerably better performance, compared to the case of using an individual modality of biomarker.
Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
This paper proposes a general methodology, namely multi-modal multi-task (M3T) learning, to jointly predict multiple variables from multi- modal data, which can achieve better performance on both regression and classification tasks than the conventional learning methods.
Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection
High-dimensional non-linear pattern classification methods applied to functional magnetic resonance images were used to discriminate between the spatial patterns of brain activity associated with lie and truth, and indicate that accurate clinical tests could be based on measurements of brain function with fMRI.
Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging
We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI),
Infant Brain Atlases from Neonates to 1- and 2-Year-Olds
It is expected that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains.
Contour Knowledge Transfer for Salient Object Detection
A contour-to-saliency transferring method to automatically generate salient object masks which can be used to train the saliency branch from outputs of the contour branch, and introduces a novel alternating training pipeline to gradually update the network parameters.
Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects
The temporal evolution of the default network in a critical, previously unstudied, period of early human brain development is described and new insights into the emergence of brain default network are offered.
Family Poverty Affects the Rate of Human Infant Brain Growth
Differences in brain growth were found to vary with socioeconomic status (SES), with children from lower-income households having slower trajectories of growth during infancy and early childhood.
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
This paper proposes to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images, and compared the performance of the approach with that of the commonly used segmentation methods on a set of manually segmented isointENSE stage brain images.
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
A deep learning (DL) based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung and possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings were discussed.