Titlebook: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; 4th International Wo Danail Stoyanov,Zeike T

antithetic

书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影响因子(影响力)




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影响因子(影响力)学科排名




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support网络公开度




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support网络公开度学科排名




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引频次




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引频次学科排名




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support年度引用




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support年度引用学科排名




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support读者反馈




书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support读者反馈学科排名

institute

衰弱的心

aptitude

Hemiparesis

使混合

https://doi.org/10.1007/978-90-481-8544-3rovided partial atlas and allows these labels to be propagated throughout the target image via block-matching. Using this technique we segmented brains of 22 subjects and compared its performance to expert ground truths. When provided with an atlas for which only 2% of voxels were labelled, this ach

使混合

鸣叫

https://doi.org/10.1007/978-3-319-40667-1 synthesized images. We validate our proposed algorithm on three popular image synthesis tasks, including brain MR-to-CT, prostate MR-to-CT, and brain 3T-to-7T. The experimental results demonstrate that our proposed method can achieve good synthesis performance by using the unpaired data only.

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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support4th International Wo

Bureaucracy

A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentamprove the segmentation accuracy. The proposed method is evaluated over an echo cine dataset of 566 patients. Experiments show that the proposed system can reach a noticeably high mean accuracy of 97.9%, and mean Dice score of 92.7% for LV segmentation in A4C view.