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

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書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影響因子(影響力)




書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影響因子(影響力)學(xué)科排名




書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support網(wǎng)絡(luò)公開度




書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引頻次




書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引頻次學(xué)科排名




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




書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support年度引用學(xué)科排名




書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support讀者反饋




書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support讀者反饋學(xué)科排名

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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

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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

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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.