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ORCIDMingxia Liu 0001
Ming-Xia Liu 0001
Person information
- affiliation: University of North Carolina, Department of Radiology and BRIC, Chapel Hill, NC, USA
- affiliation: Taishan University, School of Information Science and Technology, Tai'an, China
- affiliation (PhD 2015): Nanjing University of Aeronautics and Astronautics, School of Computer Science and Technology, Nanjing, China
Other persons with the same name
- Mingxia Liu 0002 — Shanghai University of Engineering Science, College of Electronic and Electrical Engineering, Shanghai, China
- Mingxia Liu 0003 — Southeast University, National Mobile Communications Research Laboratory, Nanjing, China
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2020 – today
- 2022
[j73]Qianqian Wang, Long Li, Lishan Qiao, Mingxia Liu:
Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection. Frontiers Neuroinformatics 16: 856175 (2022)- [j72]Yunbi Liu, Ling Yue, Shifu Xiao, Wei Yang, Dinggang Shen, Mingxia Liu:
Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages. Medical Image Anal. 75: 102266 (2022) - [j71]Nan Wang, Dongren Yao, Lizhuang Ma, Mingxia Liu:
Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI. Medical Image Anal. 75: 102279 (2022) - [j70]Hao Guan
, Mingxia Liu:
Domain Adaptation for Medical Image Analysis: A Survey. IEEE Trans. Biomed. Eng. 69(3): 1173-1185 (2022) - [j69]Chunfeng Lian, Mingxia Liu, Yongsheng Pan, Dinggang Shen:
Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images. IEEE Trans. Cybern. 52(4): 1992-2003 (2022) - [j68]Yu Zhang, Han Zhang, Ehsan Adeli, Xiaobo Chen, Mingxia Liu, Dinggang Shen:
Multiview Feature Learning With Multiatlas-Based Functional Connectivity Networks for MCI Diagnosis. IEEE Trans. Cybern. 52(7): 6822-6833 (2022) - [j67]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) - [j66]Chunfeng Lian, Mingxia Liu, Li Wang, Dinggang Shen:
Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI. IEEE Trans. Neural Networks Learn. Syst. 33(8): 4056-4068 (2022) - [i7]Hao Guan, Ling Yue, Pew-Thian Yap, Andrea Bozoki, Mingxia Liu:
Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline with Structural MRI. CoRR abs/2206.12480 (2022) - 2021
- [j65]Lei Sun, Yanfang Xue, Yining Zhang, Lishan Qiao, Limei Zhang, Mingxia Liu:
Estimating sparse functional connectivity networks via hyperparameter-free learning model. Artif. Intell. Medicine 111: 102004 (2021) - [j64]Ying Chu, Guangyu Wang, Liang Cao, Lishan Qiao, Mingxia Liu:
Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI. Frontiers Neuroinformatics 15: 802305 (2021) - [j63]Mengting Xu, Tao Zhang, Zhongnian Li, Mingxia Liu, Daoqiang Zhang:
Towards evaluating the robustness of deep diagnostic models by adversarial attack. Medical Image Anal. 69: 101977 (2021) - [j62]Mingliang Wang, Jiashuang Huang, Mingxia Liu, Daoqiang Zhang:
Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI. Medical Image Anal. 71: 102063 (2021) - [j61]Hao Guan, Yunbi Liu, Erkun Yang, Pew-Thian Yap, Dinggang Shen, Mingxia Liu:
Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Medical Image Anal. 71: 102076 (2021) - [j60]Jun Zhang, Zhiyuan Hua, Kezhou Yan, Kuan Tian, Jianhua Yao, Eryun Liu, Mingxia Liu, Xiao Han:
Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images. Medical Image Anal. 73: 102183 (2021) - [j59]Kelei He, Wei Zhao, Xingzhi Xie, Wen Ji, Mingxia Liu, Zhenyu Tang, Yinghuan Shi, Feng Shi, Yang Gao, Jun Liu, Junfeng Zhang, Dinggang Shen:
Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images. Pattern Recognit. 113: 107828 (2021) - [j58]Jun Zhang, Mingxia Liu, Ke Lu, Yue Gao:
Group-Wise Learning for Aurora Image Classification With Multiple Representations. IEEE Trans. Cybern. 51(8): 4112-4124 (2021) - [j57]Shuai Wang, Yang Cong, Hancan Zhu, Xianyi Chen, Liangqiong Qu, Huijie Fan, Qiang Zhang, Mingxia Liu:
Multi-Scale Context-Guided Deep Network for Automated Lesion Segmentation With Endoscopy Images of Gastrointestinal Tract. IEEE J. Biomed. Health Informatics 25(2): 514-525 (2021) - [j56]Shuai Wang, Mingxia Liu, Jun Lian, Dinggang Shen:
Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy. IEEE Trans. Medical Imaging 40(1): 310-320 (2021) - [j55]Erkun Yang, Mingxia Liu, Dongren Yao, Bing Cao, Chunfeng Lian, Pew-Thian Yap, Dinggang Shen:
Deep Bayesian Hashing With Center Prior for Multi-Modal Neuroimage Retrieval. IEEE Trans. Medical Imaging 40(2): 503-513 (2021) - [j54]Dongren Yao, Jing Sui, Mingliang Wang, Erkun Yang, Yeerfan Jiaerken, Na Luo, Pew-Thian Yap, Mingxia Liu, Dinggang Shen:
A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity. IEEE Trans. Medical Imaging 40(4): 1279-1289 (2021) - [c54]Erkun Yang, Lihong Wang, David C. Steffens, Guy G. Potter, Mingxia Liu:
Deep Factor Regression For Computer-Aided Analysis of Major Depressive Disorders With Structural MRI Data. ISBI 2021: 208-211 - [c53]Hao Guan, Li Wang, Mingxia Liu:
Multi-Source Domain Adaptation via Optimal Transport for Brain Dementia Identification. ISBI 2021: 1514-1517 - [c52]Hao Guan, Li Wang, Dongren Yao, Andrea Bozoki, Mingxia Liu:
Learning Transferable 3D-CNN for MRI-Based Brain Disorder Classification from Scratch: An Empirical Study. MLMI@MICCAI 2021: 10-19 - [c51]Yunbi Liu, Genggeng Qin, Yun Liu, Mingxia Liu, Wei Yang:
Improving Tuberculosis Recognition on Bone-Suppressed Chest X-Rays Guided by Task-Specific Features. PRIME@MICCAI 2021: 59-69 - [c50]Dongren Yao, Erkun Yang, Li Sun, Jing Sui, Mingxia Liu:
Integrating Multimodal MRIs for Adult ADHD Identification with Heterogeneous Graph Attention Convolutional Network. PRIME@MICCAI 2021: 157-167 - [c49]Dongren Yao, Erkun Yang, Hao Guan, Jing Sui, Zhizhong Zhang, Mingxia Liu:
Tensor-Based Multi-index Representation Learning for Major Depression Disorder Detection with Resting-State fMRI. MICCAI (5) 2021: 174-184 - [c48]Hao Guan, Yunbi Liu, Shifu Xiao, Ling Yue, Mingxia Liu:
Cost-Sensitive Meta-learning for Progress Prediction of Subjective Cognitive Decline with Brain Structural MRI. MICCAI (5) 2021: 248-258 - [c47]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 - [c46]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 - [i6]Hao Guan, Mingxia Liu:
Domain Adaptation for Medical Image Analysis: A Survey. CoRR abs/2102.09508 (2021) - [i5]Mengting Xu, Tao Zhang, Zhongnian Li, Mingxia Liu, Daoqiang Zhang:
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack. CoRR abs/2103.03438 (2021) - 2020
- [j53]Jun Zhang, Mingxia Liu, Li Wang, Si Chen, Peng Yuan, Jianfu Li, Steve Guo-Fang Shen, Zhen Tang, Ken-Chung Chen, James J. Xia, Dinggang Shen:
Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization. Medical Image Anal. 60 (2020) - [j52]Tao Zhou, Kim-Han Thung, Mingxia Liu, Feng Shi, Changqing Zhang, Dinggang Shen:
Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Medical Image Anal. 60 (2020) - [j51]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) - [j50]Mingliang Wang, Xiaoke Hao, Jiashuang Huang, Kangcheng Wang, Li Shen, Xijia Xu, Daoqiang Zhang, Mingxia Liu:
Hierarchical Structured Sparse Learning for Schizophrenia Identification. Neuroinformatics 18(1): 43-57 (2020) - [j49]Chunfeng Lian, Mingxia Liu, Jun Zhang, Dinggang Shen:
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI. IEEE Trans. Pattern Anal. Mach. Intell. 42(4): 880-893 (2020) - [j48]Mingliang Wang, Chunfeng Lian, Dongren Yao, Daoqiang Zhang, Mingxia Liu, Dinggang Shen:
Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network. IEEE Trans. Biomed. Eng. 67(8): 2241-2252 (2020) - [j47]Wei Shao, Sheng-Jun Huang, Mingxia Liu, Daoqiang Zhang:
Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages. IEEE ACM Trans. Comput. Biol. Bioinform. 17(4): 1394-1405 (2020) - [j46]Mingxia Liu, Jun Zhang, Chunfeng Lian, Dinggang Shen:
Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores. IEEE Trans. Cybern. 50(7): 3381-3392 (2020) - [j45]Liang Sun, Wei Shao, Mingliang Wang, Daoqiang Zhang, Mingxia Liu:
High-Order Feature Learning for Multi-Atlas Based Label Fusion: Application to Brain Segmentation With MRI. IEEE Trans. Image Process. 29: 2702-2713 (2020) - [j44]Yingkun Hou, Jun Xu, Mingxia Liu, Guanghai Liu, Li Liu, Fan Zhu, Ling Shao:
NLH: A Blind Pixel-Level Non-Local Method for Real-World Image Denoising. IEEE Trans. Image Process. 29: 5121-5135 (2020) - [j43]Zhenghan Fang, Yong Chen, Mingxia Liu, Lei Xiang, Qian Zhang, Qian Wang, Weili Lin, Dinggang Shen:
Erratum to "Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting". IEEE Trans. Medical Imaging 39(2): 543 (2020) - [j42]Mingliang Wang, Daoqiang Zhang, Jiashuang Huang, Pew-Thian Yap, Dinggang Shen, Mingxia Liu:
Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation. IEEE Trans. Medical Imaging 39(3): 644-655 (2020) - [j41]Liang Sun, Wei Shao, Daoqiang Zhang, Mingxia Liu:
Anatomical Attention Guided Deep Networks for ROI Segmentation of Brain MR Images. IEEE Trans. Medical Imaging 39(6): 2000-2012 (2020) - [j40]Yongsheng Pan, Mingxia Liu, Chunfeng Lian, Yong Xia, Dinggang Shen:
Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages. IEEE Trans. Medical Imaging 39(9): 2965-2975 (2020) - [c45]Weida Li, Mingxia Liu, Fang Chen, Daoqiang Zhang:
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data. AAAI 2020: 2653-2660 - [c44]Dongren Yao, Jing Sui, Erkun Yang, Pew-Thian Yap, Dinggang Shen, Mingxia Liu:
Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder Using Resting-State fMRI. MLMI@MICCAI 2020: 1-10 - [c43]Hao Guan, Erkun Yang, Pew-Thian Yap, Dinggang Shen, Mingxia Liu:
Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimaging Data. DART/DCL@MICCAI 2020: 31-40 - [c42]Hao Guan, Erkun Yang, Li Wang, Pew-Thian Yap, Mingxia Liu, Dinggang Shen:
Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network. MLMI@MICCAI 2020: 111-119 - [c41]Zhenyuan Ning, Yu Zhang, Yongsheng Pan, Tao Zhong, Mingxia Liu, Dinggang Shen:
LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI. MLMI@MICCAI 2020: 170-179 - [c40]Erkun Yang, Dongren Yao, Bing Cao, Hao Guan, Pew-Thian Yap, Dinggang Shen, Mingxia Liu:
Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search. MICCAI (1) 2020: 191-201 - [c39]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 - [c38]Yunbi Liu, Yongsheng Pan, Wei Yang, Zhenyuan Ning, Ling Yue, Mingxia Liu, Dinggang Shen:
Joint Neuroimage Synthesis and Representation Learning for Conversion Prediction of Subjective Cognitive Decline. MICCAI (7) 2020: 583-592 - [c37]Yunbi Liu, Mingxia Liu, Yuhua Xi, Genggeng Qin, Dinggang Shen, Wei Yang:
Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN. MICCAI (2) 2020: 678-687 - [e4]Mingxia Liu, Pingkun Yan, Chunfeng Lian, Xiaohuan Cao:
Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings. Lecture Notes in Computer Science 12436, Springer 2020, ISBN 978-3-030-59860-0 [contents] - [i4]Weida Li, Mingxia Liu, Daoqiang Zhang:
Nyström Subspace Learning for Large-scale SVMs. CoRR abs/2002.08937 (2020) - [i3]Kelei He, Wei Zhao, Xingzhi Xie, Wen Ji, Mingxia Liu, Zhenyu Tang, Feng Shi, Yang Gao, Jun Liu, Junfeng Zhang, Dinggang Shen:
Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images. CoRR abs/2005.03832 (2020) - [i2]Li Zhang, Mingliang Wang, Mingxia Liu, Daoqiang Zhang:
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis. CoRR abs/2005.04573 (2020)
2010 – 2019
- 2019
- [j39]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) - [j38]Liang Sun, Chen Zu, Wei Shao, Junye Guang, Daoqiang Zhang, Mingxia Liu:
Reliability-based robust multi-atlas label fusion for brain MRI segmentation. Artif. Intell. Medicine 96: 12-24 (2019) - [j37]Mingliang Wang, Daoqiang Zhang, Dinggang Shen, Mingxia Liu:
Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data. Medical Image Anal. 53: 111-122 (2019) - [j36]Jun Zhang, Mingxia Liu, Yi Zhen:
Multimedia analysis for medical applications. Multim. Syst. 25(2): 71-72 (2019) - [j35]Xuyun Wen, Han Zhang, Gang Li, Mingxia Liu, Weiyan Yin, Weili Lin, Jun Zhang, Dinggang Shen:
First-year development of modules and hubs in infant brain functional networks. NeuroImage 185: 222-235 (2019) - [j34]Yu Zhang, Han Zhang, Xiaobo Chen, Mingxia Liu, Xiaofeng Zhu, Seong-Whan Lee, Dinggang Shen:
Strength and similarity guided group-level brain functional network construction for MCI diagnosis. Pattern Recognit. 88: 421-430 (2019) - [j33]Tao Zhou, Kim-Han Thung, Mingxia Liu, Dinggang Shen:
Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model. IEEE Trans. Biomed. Eng. 66(1): 165-175 (2019) - [j32]Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen:
Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis. IEEE Trans. Biomed. Eng. 66(5): 1195-1206 (2019) - [j31]Zhenghan Fang, Yong Chen, Mingxia Liu, Lei Xiang, Qian Zhang, Qian Wang, Weili Lin, Dinggang Shen:
Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting. IEEE Trans. Medical Imaging 38(10): 2375-2388 (2019) - [j30]Tao Zhou, Mingxia Liu, Kim-Han Thung, Dinggang Shen:
Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data. IEEE Trans. Medical Imaging 38(10): 2411-2422 (2019) - [c36]Mingliang Wang, Jiashuang Huang, Mingxia Liu, Daoqiang Zhang:
Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification. AAAI 2019: 1198-1205 - [c35]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 - [c34]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 - [c33]Dongren Yao, Mingxia Liu, Mingliang Wang, Chunfeng Lian, Jie Wei, Li Sun, Jing Sui, Dinggang Shen:
Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI. GLMI@MICCAI 2019: 70-78 - [c32]Yongsheng Pan, Mingxia Liu, Chunfeng Lian, Yong Xia, Dinggang Shen:
Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-modal Neuroimages. MICCAI (3) 2019: 137-145 - [c31]Yongsheng Pan, Mingxia Liu, Li Wang, Yong Xia, Dinggang Shen:
Discriminative-Region-Aware Residual Network for Adolescent Brain Structure and Cognitive Development Analysis. GLMI@MICCAI 2019: 138-146 - [c30]Chunfeng Lian, Mingxia Liu, Li Wang, Dinggang Shen:
End-to-End Dementia Status Prediction from Brain MRI Using Multi-task Weakly-Supervised Attention Network. MICCAI (4) 2019: 158-167 - [c29]Jing Zhang, Mingxia Liu, Yongsheng Pan, Dinggang Shen:
Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI. MLMI@MICCAI 2019: 391-399 - [c28]Tao Zhou, Mingxia Liu, Huazhu Fu, Jun Wang, Jianbing Shen, Ling Shao, Dinggang Shen:
Deep Multi-modal Latent Representation Learning for Automated Dementia Diagnosis. MICCAI (4) 2019: 629-638 - [c27]Chunfeng Lian, Li Wang, Tai-Hsien Wu, Mingxia Liu, Francisca Durán, Ching-Chang Ko, Dinggang Shen:
MeshSNet: Deep Multi-scale Mesh Feature Learning for End-to-End Tooth Labeling on 3D Dental Surfaces. MICCAI (6) 2019: 837-845 - [e3]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] - [e2]Heung-Il Suk, Mingxia Liu, Pingkun Yan, Chunfeng Lian:
Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. Lecture Notes in Computer Science 11861, Springer 2019, ISBN 978-3-030-32691-3 [contents] - [i1]Yingkun Hou, Jun Xu, Mingxia Liu, Guanghai Liu, Li Liu, Fan Zhu, Ling Shao:
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising. CoRR abs/1906.06834 (2019) - 2018
- [j29]Qi Zhu, Jiuwen Zhu, Mingxia Liu, Xijia Xu, Daoqiang Zhang:
Multi-Region Correlation Based Functional Brain Network for Disease Diagnosis and Cognitive States Detection. IEEE Access 6: 78065-78076 (2018) - [j28]Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen:
Landmark-based deep multi-instance learning for brain disease diagnosis. Medical Image Anal. 43: 157-168 (2018) - [j27]Chunfeng Lian, Jun Zhang, Mingxia Liu, Xiaopeng Zong, Sheng-Che Hung, Weili Lin, Dinggang Shen:
Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images. Medical Image Anal. 46: 106-117 (2018) - [j26]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) - [j25]Wei Shao, Mingxia Liu, Ying-Ying Xu, Hong-Bin Shen, Daoqiang Zhang:
An Organelle Correlation-Guided Feature Selection Approach for Classifying Multi-Label Subcellular Bio-Images. IEEE ACM Trans. Comput. Biol. Bioinform. 15(3): 828-838 (2018) - [j24]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) - [j23]Mingxia Liu, Yue Gao, Pew-Thian Yap, Dinggang Shen:
Multi-Hypergraph Learning for Incomplete Multimodality Data. IEEE J. Biomed. Health Informatics 22(4): 1197-1208 (2018) - [j22]Mingxia Liu, Jun Zhang, Dong Nie, Pew-Thian Yap, Dinggang Shen:
Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis. IEEE J. Biomed. Health Informatics 22(5): 1476-1485 (2018) - [j21]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) - [c26]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 - [c25]Tao Zhou, Kim-Han Thung, Mingxia Liu, Feng Shi, Changqing Zhang, Dinggang Shen:
Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis. PRIME@MICCAI 2018: 76-84 - [c24]Guannan Li, Mingxia Liu, Quan-Sen Sun, Dinggang Shen, Li Wang:
Early Diagnosis of Autism Disease by Multi-channel CNNs. MLMI@MICCAI 2018: 303-309 - [c23]Zhenghan Fang, Yong Chen, Mingxia Liu, Yiqiang Zhan, Weili Lin, Dinggang Shen:
Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF). MLMI@MICCAI 2018: 398-405 - [c22]Li Wang, Gang Li, Feng Shi, Xiaohuan Cao, Chunfeng Lian, Dong Nie, Mingxia Liu, Han Zhang, Guannan Li, Zhengwang Wu, Weili Lin, Dinggang Shen:
Volume-Based Analysis of 6-Month-Old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis. MICCAI (3) 2018: 411-419 - [c21]Yongsheng Pan, Mingxia Liu, Chunfeng Lian, Tao Zhou, Yong Xia, Dinggang Shen:
Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer's Disease Diagnosis. MICCAI (3) 2018: 455-463 - [c20]Mingliang Wang, Daoqiang Zhang, Jiashuang Huang, Dinggang Shen, Mingxia Liu:
Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification. MICCAI (1) 2018: 647-654 - [c19]Yi-Lin Bei, Sai Qiao, Mingxia Liu, Xiaorong Zhu, Qian Zhang:
A Color Image Watermarking Scheme Against Geometric Rotation Attacks Based on HVS and DCT-DWT. SPAC 2018: 343-347 - [c18]Ti-Wei Tao, De-Yun Yang, Lin-Lin Wang, Ming-Xia Liu:
Effective Distance based Low Rank Representation for Image Classification. SPAC 2018: 460-464 - [e1]Yinghuan Shi, Heung-Il Suk, Mingxia Liu:
Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Lecture Notes in Computer Science 11046, Springer 2018, ISBN 978-3-030-00918-2 [contents] - 2017
- [j20]Mingxia Liu, Jun Zhang, Xiaochun Guo, Liujuan Cao:
Hypergraph regularized sparse feature learning. Neurocomputing 237: 185-192 (2017) - [j19]Jun Zhang, Qian Wang, Zejun Hu, Mingxia Liu:
Auroral event representation based on the n-ary fusion of multiple oriented energies. Neurocomputing 253: 42-48 (2017) - [j18]Mingxia Liu, Liujuan Cao, Feng Lu, Yi Zhen:
Multimodal media data understanding and analysis. Neurocomputing 259: 1-2 (2017) - [j17]Mingxia Liu, Jun Zhang, Pew-Thian Yap, Dinggang Shen:
View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modality data. Medical Image Anal. 36: 123-134 (2017) - [j16]Bo Cheng, Mingxia Liu, Dinggang Shen, Zuoyong Li, Daoqiang Zhang:
Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease. Neuroinformatics 15(2): 115-132 (2017)