Titlebook: Machine Learning in Medical Imaging; 14th International W Xiaohuan Cao,Xuanang Xu,Xi Ouyang Conference proceedings 2024 The Editor(s) (if a

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書目名稱Machine Learning in Medical Imaging影響因子(影響力)




書目名稱Machine Learning in Medical Imaging影響因子(影響力)學科排名




書目名稱Machine Learning in Medical Imaging網(wǎng)絡公開度




書目名稱Machine Learning in Medical Imaging網(wǎng)絡公開度學科排名




書目名稱Machine Learning in Medical Imaging被引頻次




書目名稱Machine Learning in Medical Imaging被引頻次學科排名




書目名稱Machine Learning in Medical Imaging年度引用




書目名稱Machine Learning in Medical Imaging年度引用學科排名




書目名稱Machine Learning in Medical Imaging讀者反饋




書目名稱Machine Learning in Medical Imaging讀者反饋學科排名

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978-3-031-45675-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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,Identifying Alzheimer’s Disease-Induced Topology Alterations in Structural Networks Using Convoluti (AD). However, conventional graph learning methods struggle to accurately represent the subtle and heterogeneous topology alterations caused by AD, leading to marginal classification accuracy. In this study, we address this issue through a two-fold approach. Firstly, to more reliably capture AD-ind

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,Specificity-Aware Federated Graph Learning for?Brain Disorder Analysis with?Functional MRI,by brain disorders. Graph neural network (GNN) has been widely used for fMRI representation learning and brain disorder analysis, thanks to its potent graph representation abilities. Training a generalizable GNN model often requires large-scale subjects from different medical centers/sites, but the

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