Conference Paper

2020

[9] Erkun Yang, Dongren Yao, Bing Cao, Hao Guan, Pew-Thian Yap, Dinggang Shen, Mingxia Liu*. Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search. The 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru, Oct. 04-08, 2020. [pdf]

[8] 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. The 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru, Oct. 04-08, 2020. [pdf] (Early Accept, Early Accept Rate 13%, Travel Award)

[7] 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. The 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru, Oct. 04-08, 2020. [pdf] (Early Accept, Early Accept Rate 13%)

TGCN[6] 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 with Resting-State fMRI. The 11th International Workshop on Machine Learning in Medical Imaging (MLMI 2020), Lima, Peru, Oct. 04, 2020. [pdf]

Ad2A[5] 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. The 2nd MICCAI Workshop on Domain Adaptation and Representation Transfer (DART 2020), Lima, Peru, Oct. 08, 2020. [pdf]

LDGAN[4] 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. The 11th International Workshop on Machine Learning in Medical Imaging (MLMI 2020), Lima, Peru, Oct. 04, 2020. [pdf]

BMI[3] 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. The 11th International Workshop on Machine Learning in Medical Imaging (MLMI 2020), Lima, Peru, Oct. 04, 2020. [pdf]

HNet[2] 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. The 11th International Workshop on Machine Learning in Medical Imaging (MLMI 2020), Lima, Peru, Oct. 04, 2020. [pdf]

[1] Weida Li, Mingxia Liu*, Daoqiang Zhang. Nyström Subspace Learning for Large-scale SVMs. arXiv:2002.08937. [pdf]

2019

[11] 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. The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, Oct. 13-17, 2019. [pdf]

[10] Chunfeng Lian, Mingxia Liu*, Li Wang, Dinggang Shen. End-to-End Dementia Status Prediction from Brain MRI using Multi-Task Weakly-Supervised Attention Network. The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, Oct. 13-17, 2019. [pdf] (Early Accept, Early Accept Rate 17%)

[9] Tao Zhou, Mingxia Liu*, Huazhu Fu, Jun Wang, Jianbing Shen, Ling Shao, Dinggang Shen. Deep Multi-modal Latent Representation Learning for Automated Dementia Diagnosis. The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, Oct. 13-17, 2019. [pdf]

[8] 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. The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, Oct. 13-17, 2019. [pdf]

[7] Mingliang Wang, Jiashuang Huang, Mingxia Liu*, Daoqiang Zhang*. Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification. The 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, USA, Jan. 27–Feb. 01, 2019. [pdf(Acceptance Rate: 16.2%, Oral Spotlight Presentation)

[6] Yongsheng Pan, Mingxia Liu, Yong Xia, Dinggang Shen. Neighborhood-Correction Algorithm for Classification of Normal and Malignant Cells. ISBI Challenge Workshop on “Classification of Normal versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images”, ISBI 2019. [pdf] (Best Performance #1 Ranked, Oral Presentation)

[5] Jing Zhang, Mingxia Liu*, Yongsheng Pan, Dinggang Shen. Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI. The 10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019), Shenzhen, China, 2019. [pdf]

[4] 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 Connectivityusing Functional MRI.  The 1st International Workshop on Graph Learning in Medical Image (GLMI 2019), Shenzhen, China, 2019. [pdf] (Best Paper Award, Oral Presentation)

[3] Yongsheng Pan, Mingxia Liu*, Li Wang, Yong Xia, Dinggang Shen. Discriminative-Region-Aware Residual Network for Adolescent Brain Structure and Cognitive Development Analysis. The 1st International Workshop on Graph Learning in Medical Image (GLMI 2019), Shenzhen, China, 2019. [pdf]

[2] Zhengdong Wang, Biao Jie, Weixin Bian, Daoqiang Zhang, Dinggang Shen, Mingxia Liu*. Adaptive Thresholding of Functional Connectivity Networks for fMRI-based Brain Disease Analysis. The 1st International Workshop on Graph Learning in Medical Image (GLMI 2019), Shenzhen, China, 2019. [pdf]

[1] 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. The 1st International Workshop on Graph Learning in Medical Image (GLMI 2019), Shenzhen, China, 2019. [pdf]

2018

[7] Mingliang Wang, Daoqiang Zhang, Jiashuang Huang, Dinggang Shen, Mingxia Liu*. Low-Rank Representation for Multi-Center Autism Spectrum Disorder Identification. The 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Granada, Spain, Sep. 16-20, 2018. [pdf] (Early Accept)

[6] 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. The 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Granada, Spain, Sep. 16-20, 2018. [pdf]

[5] Li Wang, Gang Li, Feng Shi, Xiaohuan Cao, Chunfeng Lian, Dong Nie, Mingxia Liu, Han Zhang, Guannan Li,  Weili Lin, Dinggang Shen. Volume-based Analysis of 6-month-old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis. The 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Granada, Spain, Sep. 16-20, 2018. [pdf] (Early Accept)

[4] 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. The 9th International Conference on Machine Learning in Medical Imaging (MLMI 2018), Granada, Spain, Sep. 16, 2018[pdf]

[3] Guannan Li, Mingxia Liu, Quansen Sun, Dinggang Shen, Li Wang. Early Diagnosis of Autism Disease by Multi-channel CNNs. The 9th International Conference on Machine Learning in Medical Imaging (MLMI 2018), Granada, Spain, Sep. 16, 2018. [pdf]

[2] 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). The 9th International Conference on Machine Learning in Medical Imaging (MLMI 2018), Granada, Spain, Sep. 16, 2018. [pdf]

[1] 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. The 1st  International Conference on Predictive Intelligence in Medicine (PRIME 2018), Granada, Spain, Sep. 16, 2018. [pdf]

2017

[3] Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen. Deep Multi-Task Multi-Channel Learning for Joint Classification and Regression of Brain Status. The 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Quebec, Canada, Sep. 10-14, 2017. [pdf]

[2] 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. Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks. The 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Quebec, Canada, Sep. 10-14, 2017. [pdf]

[1] Yu Zhang, Han Zhang, Xiaobo Chen, Mingxia Liu, Xiaofeng Zhu, Dinggang Shen. Inter-subject Similarity Guided Brain Network Modeling for MCI Diagnosis. International Workshop on Machine Learning in Medical Imaging (MLMI 2017), 168-175. [pdf]

2016

[5] Mingxia Liu, Jun Zhang, Pew-Thian Yap, Dinggang Shen. Diagnosis of Alzheimer’s Disease Using View-Aligned Hypergraph Learning with Incomplete Multi-modality Data. The 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), Athens, Greece, 2016. [pdf] (Early Accept, Early Acceptance Rate ~10%; MICCAI Society Young Scientist Awards Runner-up)

[4] Mingxia Liu, Junqiang Du, Biao Jie, Daoqiang Zhang. Ordinal Patterns for Connectivity Networks in Brain Disease Diagnosis. The 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), Athens, Greece, 2016. [pdf]

[3] Le An, Ehsan Adeli, Mingxia Liu, Jun Zhang, Dinggang Shen. Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer’s Disease Diagnosis. The 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), Athens, Greece, 2016. [pdf]

[2] Jun Zhang, Mingxia Liu, Le An, Dinggang Shen. Landmark-based Alzheimer’s Disease Diagnosis using Longitudinal Structural MR Images. International Workshop on Machine Learning in Medical Imaging (MLMI), Athens, Greece, 2016. [pdf] (Oral Presentation)

[1] Biao Jie, Mingxia Liu, Xi Jiang, Daoqiang Zhang. Sub-network Based Kernels for Brain Network Classification. Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Seattle, USA, 2016. [pdf]

2015

[3] Mingxia Liu, Daoqiang Zhang, Ehsan Adeli-Mosabbeb, Dinggang Shen. Relationship Induced Multi-atlas Learning for Alzheimer’s Disease Diagnosis. International Workshop on Medical Computer Vision: Algorithms for Big Data (MCV), Munich, Germany, 2015. [pdf] (Oral Presentation)

[2] Mingxia Liu, Daoqiang Zhang, Dinggang Shen. Inherent Structure-guided Multi-view Learning for Alzheimer’s Disease and Mild Cognitive Impairment Classification. International Workshop on Machine Learning in Medical Imaging (MLMI), Munich, Germany, 2015. [pdf]

[1] Bo Cheng, Mingxia Liu, Daoqiang Zhang. Mutimodal Multi-label Transfer Learning for Early Diagnosis of Alzheimer’s Disease. International Workshop on Machine Learning in Medical Imaging (MLMI), Munich, Germany, 2015. [pdf]

2012

[1] Mingxia Liu, Dan Sun, Daoqiang Zhang. Sparsity Score: A New Filter Feature Selection Method based on Graph.International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, 2012. [pdf] (Oral Presentation, Acceptance Rate ~16%)

[2] Linsong Miao, Mingxia Liu, Daoqiang Zhang. Cost-Sensitive Feature Selection with Application in Software Defect Prediction. International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, 2012. [pdf] (Oral Presentation, Acceptance Rate ~16%)

[3] Mingxia Liu, Songcan Chen, Daoqiang Zhang. Learning Attribute Relation in Attribute-based Zero-shot Classification. Sino-foreign-interchange Workshop on Intelligence Science & Intelligent Data Engineering (ISciDE), Nanjing, China, 2012. [pdf]

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