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Titlebook: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016; 19th International C Sebastien Ourselin,Leo Joskowicz,William Wel

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發(fā)表于 2025-3-25 04:31:39 | 只看該作者
Neeraj Dhungel,Gustavo Carneiro,Andrew P. Bradleynd Learning (CRYSTAL) funded for 5 years (2005–2010) by the Natural Sciences and Engineering Research Council Canada (NSERC). Pacific CRYSTAL intended to promote scientific, mathematical, and technological literacy for responsible citizenship through research partnerships with university and educati
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發(fā)表于 2025-3-25 09:12:09 | 只看該作者
Tao Xu,Han Zhang,Xiaolei Huang,Shaoting Zhang,Dimitris N. Metaxasl Literacy is one of five Centres for Research into Youth, Science Teaching and Learning (CRYSTAL) funded for 5 years (2005–2010) by the Natural Sciences and Engineering Research Council Canada (NSERC). Pacific CRYSTAL intended to promote scientific, mathematical, and technological literacy for resp
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發(fā)表于 2025-3-25 15:16:23 | 只看該作者
Wei Shen,Mu Zhou,Feng Yang,Di Dong,Caiyun Yang,Yali Zang,Jie Tiannd Learning (CRYSTAL) funded for 5 years (2005–2010) by the Natural Sciences and Engineering Research Council Canada (NSERC). Pacific CRYSTAL intended to promote scientific, mathematical, and technological literacy for responsible citizenship through research partnerships with university and educati
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26#
發(fā)表于 2025-3-26 01:29:28 | 只看該作者
Qi Dou,Hao Chen,Yueming Jin,Lequan Yu,Jing Qin,Pheng-Ann Hengnd Learning (CRYSTAL) funded for 5 years (2005–2010) by the Natural Sciences and Engineering Research Council Canada (NSERC). Pacific CRYSTAL intended to promote scientific, mathematical, and technological literacy for responsible citizenship through research partnerships with university and educati
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發(fā)表于 2025-3-26 11:34:18 | 只看該作者
Stability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis,learning to obtain a stable set of weights. Incorporating these weights into LRMC, swLRMC can better account for differences in features and modalities for improving diagnosis. Experimental results confirm that the proposed method outperforms the conventional LRMC, feature-selection based LRMC, and other state-of-the-art methods.
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