重婚
发表于 2025-3-21 20:07:27
书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0629202<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0629202<br><br> <br><br>
语言学
发表于 2025-3-21 23:15:54
978-3-030-59712-2Springer Nature Switzerland AG 2020
血统
发表于 2025-3-22 03:10:37
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SNEER
发表于 2025-3-22 06:42:58
https://doi.org/10.1007/978-3-030-59713-9artificial intelligence; bioinformatics; color image processing; computer aided diagnosis; computer visi
寻找
发表于 2025-3-22 11:37:59
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LAITY
发表于 2025-3-22 15:32:49
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天空
发表于 2025-3-22 17:11:05
Active MR ,-space Sampling with Reinforcement Learningion process and propose the use of reinforcement learning to solve it. Experiments on a large scale public MRI dataset of knees show that our proposed models significantly outperform the state-of-the-art in active MRI acquisition, over a large range of acceleration factors.
协迫
发表于 2025-3-23 00:22:29
End-to-End Variational Networks for Accelerated MRI Reconstructionemained an open problem. In this paper, we present a new approach to this problem that extends previously proposed variational methods by learning fully end-to-end. Our method obtains new state-of-the-art results on the fastMRI dataset [.] for both brain and knee MRIs.
frugal
发表于 2025-3-23 04:54:59
Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network-based convolutional neural network that caters to non-Cartesian spiral trajectories commonly used for MRF acquisition. We improve tissue quantification accuracy compared with the state of the art. Our method enables fast 3D MRF with high spatial resolution, allowing whole-brain coverage within 5 min, making MRF more feasible in clinical settings.
exacerbate
发表于 2025-3-23 07:56:01
Conference proceedings 2020e on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic..The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review