Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

重婚

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影響因子(影響力)




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影響因子(影響力)學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020網(wǎng)絡(luò)公開度




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引頻次




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引頻次學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用學(xué)科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020讀者反饋




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020讀者反饋學(xué)科排名

語言學(xué)

978-3-030-59712-2Springer Nature Switzerland AG 2020

血統(tǒng)

SNEER

https://doi.org/10.1007/978-3-030-59713-9artificial intelligence; bioinformatics; color image processing; computer aided diagnosis; computer visi

尋找

LAITY

天空

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.

協(xié)迫

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

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

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