Titlebook: Information Processing in Medical Imaging; 27th International C Aasa Feragen,Stefan Sommer,Mads Nielsen Conference proceedings 2021 Springe

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书目名称Information Processing in Medical Imaging影响因子(影响力)




书目名称Information Processing in Medical Imaging影响因子(影响力)学科排名




书目名称Information Processing in Medical Imaging网络公开度




书目名称Information Processing in Medical Imaging网络公开度学科排名




书目名称Information Processing in Medical Imaging被引频次




书目名称Information Processing in Medical Imaging被引频次学科排名




书目名称Information Processing in Medical Imaging年度引用




书目名称Information Processing in Medical Imaging年度引用学科排名




书目名称Information Processing in Medical Imaging读者反馈




书目名称Information Processing in Medical Imaging读者反馈学科排名

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Deep Learning Based Geometric Registration for Medical Images: How Accurate Can We Get Without Visuae registration. Multi-scale encoder-decoder network architectures achieve state-of-the-art accuracy on tasks such as intra-patient alignment of abdominal CT or brain MRI registration, especially when additional supervision, such as anatomical labels, is available. The success of these methods relies

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Multiple-Shooting Adjoint Method for Whole-Brain Dynamic Causal Modelingns between underlying neural populations based on functional neuroimaging data. DCM is typically analyzed with the expectation-maximization (EM) algorithm. However, because the inversion of a large-scale continuous system is difficult when noisy observations are present, DCM by EM is typically limit

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Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Trainingzation performance. In medical image analysis, a well-designed augmentation policy usually requires much expert knowledge and is difficult to generalize to multiple tasks due to the vast discrepancies among pixel intensities, image appearances, and object shapes in different medical tasks. To automa

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