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Titlebook: Brain Informatics; 16th International C Feng Liu,Yu Zhang,Hongjun Wang Conference proceedings 2023 The Editor(s) (if applicable) and The Au

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發(fā)表于 2025-3-23 11:37:52 | 只看該作者
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發(fā)表于 2025-3-23 17:26:20 | 只看該作者
Normale Anatomie und Anlagevarianten,tionship between the performance of ESI methods and the number of scalp EEG electrodes has been a topic of ongoing investigation. Research has shown that high-density EEG is necessary for obtaining accurate and reliable ESI results using conventional ESI solutions, limiting their applications when o
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發(fā)表于 2025-3-23 21:02:55 | 只看該作者
Normale Anatomie und Anlagevarianten,tivity can be more precise with fMRI data. However, it is still difficult to obtain addiction-related brain connectivity effectively from fMRI data due to the complexity and non-linear characteristics of brain connections. Therefore, this paper proposed a Graph Diffusion Reconstruction Network (GDRN
14#
發(fā)表于 2025-3-24 00:09:54 | 只看該作者
https://doi.org/10.1007/3-540-32860-2) can detect and track disease progression. However, the majority of MRI data currently available is characterized by low resolution. The present study introduces a novel approach for MRI super-resolution by integrating diffusion model with wavelet decomposition techniques. The methodology proposed
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發(fā)表于 2025-3-24 04:55:57 | 只看該作者
16#
發(fā)表于 2025-3-24 06:51:12 | 只看該作者
,Traumatische Ver?nderungen, Frakturen,as included studies with relatively small sample sizes across research sites, thus limiting inference and the application of novel methods, such as deep learning. To address these issues and facilitate open science, we developed an online platform for data-sharing and advanced research programs to e
17#
發(fā)表于 2025-3-24 11:11:49 | 只看該作者
,Tumoren und tumor?hnliche L?sionen,pproaches. However, typical ML classifiers are not able to provide information on time and risk to AD conversion. Survival Analysis statistical methods as Cox Proportional Hazard (CPH) give this information and can handle censored data, but they were designed for small dataset and do not perform wel
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發(fā)表于 2025-3-24 18:13:52 | 只看該作者
19#
發(fā)表于 2025-3-24 20:36:10 | 只看該作者
https://doi.org/10.1007/978-3-031-43075-6Cognitive Science; Neuroscience; Machine Learning; Data Science; Artificial Intelligence (AI); Informatio
20#
發(fā)表于 2025-3-24 23:24:31 | 只看該作者
978-3-031-43074-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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