次要 发表于 2025-3-21 16:59:30
书目名称Machine Learning in Medical Imaging影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0620681<br><br> <br><br>书目名称Machine Learning in Medical Imaging读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0620681<br><br> <br><br>爱了吗 发表于 2025-3-21 23:46:27
http://reply.papertrans.cn/63/6207/620681/620681_2.pnghypertension 发表于 2025-3-22 02:41:45
978-3-031-45675-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl收到 发表于 2025-3-22 05:40:28
http://reply.papertrans.cn/63/6207/620681/620681_4.pngEmbolic-Stroke 发表于 2025-3-22 09:59:11
http://reply.papertrans.cn/63/6207/620681/620681_5.pngOutspoken 发表于 2025-3-22 14:19:05
http://reply.papertrans.cn/63/6207/620681/620681_6.png西瓜 发表于 2025-3-22 19:47:06
http://reply.papertrans.cn/63/6207/620681/620681_7.png方舟 发表于 2025-3-22 21:28:29
,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和平 发表于 2025-3-23 04:58:00
,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惊奇 发表于 2025-3-23 08:45:50
http://reply.papertrans.cn/63/6207/620681/620681_10.png