Titlebook: Biomedical Image Registration; 10th International W Alessa Hering,Julia Schnabel,Daniel Rueckert Conference proceedings 2022 The Editor(s)

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書目名稱Biomedical Image Registration影響因子(影響力)




書目名稱Biomedical Image Registration影響因子(影響力)學科排名




書目名稱Biomedical Image Registration網(wǎng)絡公開度




書目名稱Biomedical Image Registration網(wǎng)絡公開度學科排名




書目名稱Biomedical Image Registration被引頻次




書目名稱Biomedical Image Registration被引頻次學科排名




書目名稱Biomedical Image Registration年度引用




書目名稱Biomedical Image Registration年度引用學科排名




書目名稱Biomedical Image Registration讀者反饋




書目名稱Biomedical Image Registration讀者反饋學科排名

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,Building the ‘Great March’ of Progress,earning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks. The method is inspired by the recent literature on deformable image registration with adversarial learning. We combine the best performing generative, discriminative, and adversarial ingredients from the

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The Dynamics of Capitalist Development,y when predicting domain-shifted input data. Multi-atlas segmentation utilizes multiple available sample annotations which are deformed and propagated to the target domain via multimodal image registration and fused to a consensus label afterwards but subsequent network training with the registered

Gene408

,Coming to the Forefront, 1883–1931,istration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. However, these methods cover only a limited degree of deformations. We address this limitation and define an approximate metric space for the manifold of diffeomorphis

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Hypomania

https://doi.org/10.1057/9780230512320linear registration to reduce the inter-individual variability. This assumption is challenged here. Regional anatomical and connection patterns cluster into statistically distinct types. An advanced analysis proposed here leads to a deeper understanding of the governing principles of cortical variab

Enteropathic

https://doi.org/10.1007/978-3-319-64692-3ging step because of the heterogeneous content of the human abdomen which implies complex deformations. In this work, we focus on accurately registering a subset of organs of interest. We register organ surface point clouds, as may typically be extracted from an automatic segmentation pipeline, by e

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https://doi.org/10.1007/978-3-319-64692-3udes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or

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