Titlebook: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; 4th International Wo Danail Stoyanov,Zeike T - BOOKS with Alphabet D (Da, Db,Dc, Dd, De…... ) - 派博传思国际中心

antithetic 发表于 2025-3-21 19:52:46

书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影响因子(影响力) http://impactfactor.cn/2024/if/?ISSN=BK0264622 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 http://impactfactor.cn/2024/ifr/?ISSN=BK0264622 书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support网络公开度 http://impactfactor.cn/2024/at/?ISSN=BK0264622 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 http://impactfactor.cn/2024/atr/?ISSN=BK0264622 书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引频次 http://impactfactor.cn/2024/tc/?ISSN=BK0264622 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 http://impactfactor.cn/2024/tcr/?ISSN=BK0264622 书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support年度引用 http://impactfactor.cn/2024/ii/?ISSN=BK0264622 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 http://impactfactor.cn/2024/iir/?ISSN=BK0264622 书目名称Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support读者反馈 http://impactfactor.cn/2024/5y/?ISSN=BK0264622 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 http://impactfactor.cn/2024/5yr/?ISSN=BK0264622

institute 发表于 2025-3-21 20:49:45

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衰弱的心 发表于 2025-3-22 01:42:59

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aptitude 发表于 2025-3-22 04:35:35

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Hemiparesis 发表于 2025-3-22 12:26:13

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使混合 发表于 2025-3-22 14:42:42

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 发表于 2025-3-22 20:40:26

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鸣叫发表于 2025-3-22 23:28:33

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.

Urea508 发表于 2025-3-23 01:53:18

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

Bureaucracy 发表于 2025-3-23 08:17:36

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.

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