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浏览/检索结果: 共8条，第1-8条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery 期刊论文 REMOTE SENSING, 2020, 卷号: 12, 期号: 7 作者:  Pyo, JongCheol;  Duan, Hongtao;  Ligaray, Mayzonee;  Kim, Minjeong;  Baek, Sangsoo 收藏  |  浏览/下载：7/0  |  提交时间：2021/11/15 Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification 期刊论文 Neuroinformatics, 2019 作者:  Li Yang;  Liu Jingyu;  Peng Ziwen;  Sheng Can;  Kim Minjeong 收藏  |  浏览/下载：14/0  |  提交时间：2019/12/30 Computer-aided detection and diagnosis  High-order network  Low-order network  Mild cognitive impairment  Ultra-least squares   Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification 期刊论文 MEDICAL IMAGE ANALYSIS, 2019, 卷号: 52, 页码: 80-96 作者:  Li, Yang;  Liu, Jingyu;  Gao, Xinqiang;  Jie, Biao;  Kim, Minjeong 收藏  |  浏览/下载：9/0  |  提交时间：2019/12/30 Hyper-connectivity network  Multimodality  Weighted LASSO  Mild cognitive impairment (MCI)  Arterial spin labeling (ASL)  Ultra-least squares (ULS)   Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification 期刊论文 Machine learning in medical imaging. MLMI (Workshop), 2017, 卷号: 10541, 页码: 299-306 作者:  Li Yang;  Liu Jingyu;  Luo Meilin;  Li Ke;  Yap Pew-Thian 收藏  |  浏览/下载：14/0  |  提交时间：2019/12/30 Fusion of High-Order and Low-Order Effective Connectivity Networks for MCI Classification 会议论文 MACHINE LEARNING IN MEDICAL IMAGING (MLMI 2017), 2017-01-01 作者:  Li, Yang;  Liu, Jingyu;  Li, Ke;  Yap, Pew-Thian;  Kim, Minjeong 收藏  |  浏览/下载：9/0  |  提交时间：2019/12/30 Novel Effective Connectivity Network Inference for MCI Identification 会议论文 MACHINE LEARNING IN MEDICAL IMAGING (MLMI 2017), 2017-01-01 作者:  Li, Yang;  Yang, Hao;  Li, Ke;  Yap, Pew-Thian;  Kim, Minjeong 收藏  |  浏览/下载：7/0  |  提交时间：2019/12/30 Predict brain MR image registration via sparse learning of appearance and transformation 期刊论文 MEDICAL IMAGE ANALYSIS, 2015, 卷号: 20, 期号: 1 作者:  Wang, Qian;  Kim, Minjeong;  Shi, Yonghong;  Wu, Guorong;  Shen, Dinggang 收藏  |  浏览/下载：13/0  |  提交时间：2019/12/19 Deformable image registration  Brain MR image registration  Transformation prediction  Correspondence detection  Sparsity learning   Improving accuracy of automatic hippocampus segmentation in routine MRI by features learned from ultra-high field MRI 会议论文 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015, Munich, Germany, 2015-10-09 作者:  Li, Shuyu;  Shi, Feng;  Ma, Guangkai;  Kim, Minjeong;  Shen, Dinggang 收藏  |  浏览/下载：5/0  |  提交时间：2020/01/06