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Biao Jie
Publications
- 2022
- [j22]Zhengdong Wang, Biao Jie, Chunxiang Feng, Taochun Wang, Weixin Bian, Xintao Ding, Wen Zhou, Mingxia Liu:
Distribution-Guided Network Thresholding for Functional Connectivity Analysis in fMRI-Based Brain Disorder Identification. IEEE J. Biomed. Health Informatics 26(4): 1602-1613 (2022) - 2021
- [c21]Kai Lin, Biao Jie, Peng Dong, Xintao Ding, Weixin Bian, Mingxia Liu:
Extracting Sequential Features from Dynamic Connectivity Network with rs-fMRI Data for AD Classification. MLMI@MICCAI 2021: 664-673 - [c20]Peng Dong, Biao Jie, Lin Kai, Xintao Ding, Weixin Bian, Mingxia Liu:
Integration of Handcrafted and Embedded Features from Functional Connectivity Network with rs-fMRI forBrain Disease Classification. MLMI@MICCAI 2021: 674-681 - 2020
- [j17]Biao Jie, Mingxia Liu, Chunfeng Lian, Feng Shi, Dinggang Shen:
Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis. Medical Image Anal. 63: 101709 (2020) - [c19]Chunxiang Feng, Biao Jie, Xintao Ding, Daoqiang Zhang, Mingxia Liu:
Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification. MLMI@MICCAI 2020: 303-311 - 2019
- [j14]Mi Wang, Biao Jie, Weixin Bian, Xintao Ding, Wen Zhou, Zhengdong Wang, Mingxia Liu:
Graph-Kernel Based Structured Feature Selection for Brain Disease Classification Using Functional Connectivity Networks. IEEE Access 7: 35001-35011 (2019) - [c18]Zhengdong Wang, Biao Jie, Weixin Bian, Daoqiang Zhang, Dinggang Shen, Mingxia Liu:
Adaptive Thresholding of Functional Connectivity Networks for fMRI-Based Brain Disease Analysis. GLMI@MICCAI 2019: 18-26 - [c17]Zhengdong Wang, Biao Jie, Mi Wang, Chunxiang Feng, Wen Zhou, Dinggang Shen, Mingxia Liu:
Graph-Kernel-Based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification. GLMI@MICCAI 2019: 27-35 - [e1]Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu:
Graph Learning in Medical Imaging - First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. Lecture Notes in Computer Science 11849, Springer 2019, ISBN 978-3-030-35816-7 [contents] - 2018
- [j10]Biao Jie, Mingxia Liu, Dinggang Shen:
Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease. Medical Image Anal. 47: 81-94 (2018) - [j9]Biao Jie, Mingxia Liu, Daoqiang Zhang, Dinggang Shen:
Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis. IEEE Trans. Image Process. 27(5): 2340-2353 (2018) - [j8]Daoqiang Zhang, Jiashuang Huang, Biao Jie, Junqiang Du, Liyang Tu, Mingxia Liu:
Ordinal Pattern: A New Descriptor for Brain Connectivity Networks. IEEE Trans. Medical Imaging 37(7): 1711-1722 (2018) - [c16]Biao Jie, Mingxia Liu, Chunfeng Lian, Feng Shi, Dinggang Shen:
Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis. MLMI@MICCAI 2018: 1-9 - 2017
- [j7]Biao Jie, Mingxia Liu, Jun Liu, Daoqiang Zhang, Dinggang Shen:
Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease. IEEE Trans. Biomed. Eng. 64(1): 238-249 (2017) - 2016
- [c14]Biao Jie, Mingxia Liu, Xi Jiang, Daoqiang Zhang:
Sub-network Based Kernels for Brain Network Classification. BCB 2016: 622-629 - [c12]Mingxia Liu, Junqiang Du, Biao Jie, Daoqiang Zhang:
Ordinal Patterns for Connectivity Networks in Brain Disease Diagnosis. MICCAI (1) 2016: 1-9
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