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Dna262

Graph-Kernel-Based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Nases, such as Alzheimer’s disease (AD) and Attention Deficit Hyperactivity Disorder (ADHD). However, existing studies usually extract meaningful measures (., local clustering coefficients) from FCNs as a feature vector for brain disease classification, and perform vector-based feature selection meth

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GRAIN

Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-

OASIS

Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motio from the recent advances in medical deep learning, the same does not yet apply for the registration of point sets, e.g. registration based on surfaces, keypoints or landmarks. This is mainly due to the restriction of the convolution operator in modern CNNs to densely gridded input. However, with th

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坚毅

Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functtivity. Using fMRI data, graph convolutional network (GCN) has recently shown its superiority in learning discriminative representations of brain FC networks. However, existing studies typically utilize one specific template to partition the brain into multiple regions-of-interest (ROIs) for constru

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Multi-scale Graph Convolutional Network for Mild Cognitive Impairment Detection,ly consider neuroimaging features learned from group relationships instead of the subjects’ individual features. Such methods ignore demographic relationships (i.e., non-image information). In this paper, we propose a novel method based on multi-scale graph convolutional network (MS-GCN) via incepti

松紧带

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks,r parcellation approaches rely on accurate registration between an atlas and the tractograms of an individual, however, due to large individual differences, accurate registration is hard to guarantee in practice. To resolve this issue, we propose a novel deep learning method, called DeepBundle, for