Titlebook: Machine Learning in Medical Imaging; 13th International W Chunfeng Lian,Xiaohuan Cao,Zhiming Cui Conference proceedings 2022 Springer Natur

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Linkai Peng,Li Lin,Pujin Cheng,Huaqing He,Xiaoying Tangs of aging research for some time. For example, the mTOR pathway, a regulator of translation and protein synthesis, has been identified as a common longevity pathway in yeast and Caenorhabditis elegans. In mamm978-1-61779-747-7978-1-60327-507-1

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Ken C. L. Wong,Mehdi Moradis of aging research for some time. For example, the mTOR pathway, a regulator of translation and protein synthesis, has been identified as a common longevity pathway in yeast and Caenorhabditis elegans. In mamm978-1-61779-747-7978-1-60327-507-1

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,Function MRI Representation Learning via?Self-supervised Transformer for?Automated Brain Disorder Asonance imaging (rs-fMRI) has been used to capture abnormality or dysfunction functional connectivity networks for automated MDD detection. A functional connectivity network (FCN) of each subject derived from rs-fMRI data can be modeled as a graph consisting of nodes and edges. Graph neural networks

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,Predicting Age-related Macular Degeneration Progression with?Longitudinal Fundus Images Using Deep e. While existing risk prediction models for progression to late AMD are useful for triaging patients, none utilizes longitudinal color fundus photographs (CFPs) in a patient’s history to estimate the risk of late AMD in a given subsequent time interval. In this work, we seek to evaluate how deep ne

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,Region-Guided Channel-Wise Attention Network for?Accelerated MRI Reconstruction,rs its development in time-critical applications. In recent years, deep learning-based methods leverage the powerful representations of neural networks to recover high-quality MR images from undersampled measurements, which shortens the acquisition process and enables accelerated MRI scanning. Despi

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,Student Becomes Decathlon Master in?Retinal Vessel Segmentation via?Dual-Teacher Multi-target Domaiributions. However, most of them only focus on single-target domain adaptation and cannot be applied to the scenario with multiple target domains. In this paper, we propose RVms, a novel unsupervised multi-target domain adaptation approach to segment retinal vessels (RVs) from multimodal and multice