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

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書目名稱Information Processing in Medical Imaging影響因子(影響力)




書目名稱Information Processing in Medical Imaging影響因子(影響力)學(xué)科排名




書目名稱Information Processing in Medical Imaging網(wǎng)絡(luò)公開度




書目名稱Information Processing in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Information Processing in Medical Imaging被引頻次




書目名稱Information Processing in Medical Imaging被引頻次學(xué)科排名




書目名稱Information Processing in Medical Imaging年度引用




書目名稱Information Processing in Medical Imaging年度引用學(xué)科排名




書目名稱Information Processing in Medical Imaging讀者反饋




書目名稱Information Processing in Medical Imaging讀者反饋學(xué)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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