BILE
发表于 2025-3-26 22:28:24
Constraining Disease Progression Models Using Subject Specific Connectivity Priors,imental results on a subset of the Alzheimer’s Disease Neuroimaging Initiative data set (ADNI 2). Though trained solely on cross-sectional data, our model successfully assigns higher progression scores to patients converting to more severe stages of dementia.
Neutral-Spine
发表于 2025-3-27 03:55:02
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/235639.jpg
侵略主义
发表于 2025-3-27 09:13:56
https://doi.org/10.1007/978-3-030-32391-2artificial intelligence; brain connectivity; classification; data mining; diffusion MRI; feature selectio
GRILL
发表于 2025-3-27 13:21:03
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arthrodesis
发表于 2025-3-27 14:42:50
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Legend
发表于 2025-3-27 20:56:43
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Derogate
发表于 2025-3-28 01:16:57
https://doi.org/10.1007/0-387-27636-Xvity is a popular approach in investigating the relationship between the brain morphology, structure, and function and the emergence of neurological diseases. However, extracting relevant diagnostic information from the connectome is still one of the most challenging problems. Many works have thorou
Allowance
发表于 2025-3-28 05:23:55
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ERUPT
发表于 2025-3-28 07:32:52
https://doi.org/10.1007/0-387-27636-Xy possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributi
寻找
发表于 2025-3-28 12:03:19
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