Titlebook: Machine Learning in Clinical Neuroimaging; 6th International Wo Ahmed Abdulkadir,Deepti R. Bathula,Yiming Xiao Conference proceedings 2023

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620661.jpg

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Machine Learning in Clinical Neuroimaging978-3-031-44858-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

歌唱隊(duì)

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Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease wilationship between tau and neuronal hypometabolism with positron emission tomography (PET) has been studied by T/N regression models, there has been limited application of image-to-image translation to compare between AD biomarker domains. We optimize a contrastive learning (CL) generative adversari

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Multi-shell dMRI Estimation from Single-Shell Data via Deep Learninges compartmental modeling of brain tissues as well as enhanced estimation of white matter fiber orientations via the orientation distribution function (ODF). However, multi-shell dMRI acquisitions are time consuming, expensive and difficult in certain clinical populations. We present a method to est

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Accolade

VesselShot: Few-shot Learning for?Cerebral Blood Vessel Segmentationar network from different imaging modalities, deep learning (DL) has emerged as a promising approach. However, existing DL methods often depend on proprietary datasets and extensive manual annotation. Moreover, the availability of pre-trained networks specifically for medical domains and 3D volumes

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WaveSep: A Flexible Wavelet-Based Approach for?Source Separation in?Susceptibility Imaginge biological functions and health conditions of the brain. However, general and flexible deep-learning-based tools that can provide this information in humans . are limited. For instance, the state-of-the-art deep-learning-based source separation method in quantitative susceptibility mapping (QSM) d