Titlebook: ;

arthrodesis

https://doi.org/10.1007/978-3-662-08744-2 to eliminate the variability of anatomical structure and functional topography of the human brain, which calls for aligning fMRI data across subjects. However, the existing methods do not exploit the geometry of the stimuli, which can be inferred by using certain domain knowledge and then serve as

變形

褻瀆

https://doi.org/10.1007/978-3-658-02813-8of brain diseases, such as attention deficit hyperactivity disorder (ADHD) and Alzheimer’s disease (AD). To generate compact representations of FC networks for disease analysis, various thresholding strategies have been developed for analyzing brain FC networks. However, existing studies typically e

Cuisine

https://doi.org/10.1007/978-3-658-30067-8ases, 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

熱心助人

https://doi.org/10.1007/978-3-322-87806-9n learn features from Euclidean data such as images. In this work, we propose a novel method to combine CNNs with GCNs (CNN-GCN), that can consider both Euclidean and non-Euclidean features and can be trained end-to-end. We applied this method to separate the pulmonary vascular trees into arteries a

frozen-shoulder

https://doi.org/10.1007/978-3-7091-7620-7 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-

Carbon-Monoxide

https://doi.org/10.1007/b138008 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

倔強(qiáng)不能

https://doi.org/10.1007/978-3-7091-8940-5his work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices

coagulation

https://doi.org/10.1007/978-3-7091-9073-9tivity. 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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