1. Jun Zhang, Mingxia Liu, Ke Lu, Yue Gao. Group-Wise Learning for Aurora Image Classification with Multiple Representations. IEEE Transactions on Cybernetics2019. (Accepted)
  2. Jun Zhang, Ashirbani Saha, Zhe Zhu, Maciej A. Mazurowski. Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomicsIEEE Transactions on Medical Imaging, 38(2): 435-447, 2019.
  3. Mingxia Liu*, Jun Zhang*, Chunfeng Lian, Dinggang Shen. “Weakly-Supervised Deep Learning for Brain Disease Prognosis using MRI and Incomplete Clinical Scores”. IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2019.2904186, 2019. (*Co-first Author, Accepted)
  4. Chunfeng Lian, Mingxia Liu, Jun Zhang, Dinggang Shen. Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRIIEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2018.2889096, 2019. (In press)
  5. Eryun Liu, Kangping Chen, Zhiyu Xiang, Jun Zhang. Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing. Journal of Intelligent Manufacturing, 2019. (Accepted)
  6. Mingxia Liu*, Jun Zhang*, Ehsan Adeli, Dinggang Shen. Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer’s Disease DiagnosisIEEE Transactions on Biomedical Engineering, 66 (5): 1195-1206, 2019. (*Co-first Author)


  1. Jun Zhang, Ashirbani Saha, Brian J. Soher, Maciej A. Mazurowski, Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images, arXiv preprint arXiv:1807.02152, 2018.
  2. Chuang Niu, Jun Zhang, Qian Wang, Jimin Liang. Weakly Supervised Learning for Joint Key Local Structure Localization and Classification of Aurora Image, IEEE Transactions on Geoscience and Remote Sensing, 2018. DOI: 10.1109/TGRS.2018.2848725 (In press)
  3. Mingxia Liu*, Jun Zhang*, Dong Nie, Pew-Thian Yap, Dinggang Shen. Anatomical Landmark based Deep Feature Representation for MR Images in Brain Disease DiagnosisIEEE Journal of Biomedical and Health Informatics, 22(5): 1476-1485, 2018. (*Co-first Author)
  4. Mingxia Liu*, Jun Zhang*, Ehsan Adeli, Dinggang Shen. Landmark-based Deep Multi-Instance Learning for Brain Disease DiagnosisMedical Image Analysis, 43: 157-168, 2018.(*Co-first Author)
  5. Liang Cao, Long Li, Jifeng Zheng, Xin Fan, Feng Yin, Hui Shen, Jun Zhang#. Multi-task Neural Networks for Joint Hippocampus Segmentation and Clinical Score RegressionMultimedia Tools and Applications, 1-18, 2018. (#Correspoinding Author)
  6. Chunfeng Lian*, Jun Zhang*, Mingxia Liu, Xiaoping Zong, Weili Lin, Dinggang Shen. Multi-Channel Multi-Scale Fully Convolutional Network for 3D Perivascular Spaces Segmentation in 7T MR ImagesMedical Image Analysis, 46: 106-117, 2018. (*Co-first Author)
  7. Xiaohuan Cao, Jianhua Yang, Jun Zhang, Qian Wang, Pew-Thian Yap, Dinggang Shen. Deformable Image Registration Using Cue-aware Deep Regression NetworkIEEE Transaction on Biomedical Engineering, 65(9):1900-1911. (Covered Article)


  1. Jun Zhang, Mingxia Liu, Dinggang Shen. Detecting Anatomical Landmarks from Limited Medical Imaging Data using Two-Stage Task-Oriented Deep Neural NetworksIEEE Trans. on Image Processing, 26(10): 4753-4764, 2017.
  2. Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen. Alzheimer’s Disease Diagnosis using Landmark-based Features from Longitudinal Structural MR ImagesIEEE Journal of Biomedical and Health Informatics, 21(3): 1607-1616, 2017
  3. Jun Zhang, Yaozong Gao, Sang Hyun Park, Xiaopeng Zong, Weili Lin, Dinggang Shen. “Structured Learning for 3D Perivascular Spaces Segmentation Using Vascular Features.” IEEE Trans. on Biomedical Engineering, 64(12): 2803-2812, 2017. (Featured Article)
  4. Jun Zhang, Qian Wang, Zejun Hu, Mingxia Liu.  Auroral Event Representation based on the N-ary Fusion of Multiple Oriented EnergiesNeurocomputing 253: 42-48, 2017.
  5. Jun Zhang, Jimin Liang, Haihong Hu. Multi-view texture classification using hierarchical synthetic imagesMultimedia Tools and Applications 76(16), 17511-17523, 2017
  6. Mingxia Liu, Jun Zhang, Pew-Thian Yap, Dinggang Shen. View-Aligned Hypergraph Learning for Alzheimer’s Disease Diagnosis with Incomplete Multi-modality DataMedical Image Analysis, 36(2): 123-134, 2017.
  7. Zhenzhen, Xu, Bo Tao, Yu Li, Jun Zhang, Xiaochao Qu, Feng Cao, Jimin Liang. 3D Fusion Framework for Infarction and Angiogenesis Analysis in a Myocardial Infarct Minipig ModelMolecular Imaging 16: 1536012117708735, 2017.
  8. Yingkun Hou, Sang Hyun Park, Qian Wang, Jun Zhang, Xiaopeng Zong, Weili Lin, and Dinggang Shen. Enhancement of Perivascular Spaces in 7T MR Image using Haar Transform of Non-local Cubes and Block-matching FilteringScientific Reports 7, 2017.
  9. Le An, Ehsan Adeli, Mingxia Liu, Jun Zhang, Seong-Whan Lee, Dinggang Shen. A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease DiagnosisScientific Reports 7, 2017.
  10. Liu, Mingxia, Jun Zhang#, Xiaochun Guo, and Liujuan Cao. Hypergraph Regularized Sparse Feature LearningNeurocomputing. 237: 185-192, 2017. (#Correspoinding Author)
  11. Z Zhu, E Albadawy, A Saha, Jun Zhang, MR Harowicz, MA Mazurowski. Deep Learning for Identifying Radiogenomic Associations in Breast Cancer, arXiv preprint arXiv:1711.11097.
  12. Z Zhu, M Harowicz, Jun Zhang, A Saha, LJ Grimm, ES Hwang. Deep Learning Analysis of Breast MRIs for Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ, arXiv preprint arXiv:1711.10577.


  1. Jun Zhang, Yue Gao, Yaozong Gao, Munsell Brent, and Dinggang Shen. Detecting Anatomical Landmarks for Fast Alzheimer’s Disease DiagnosisIEEE Trans. on Medical Imaging, 35(12): 2524-2533, 2016.
  2. Jun Zhang, Yaozong Gao, Li Wang, Zhen Tang, James J. Xia, and Dinggang Shen. Automatic Craniomaxillofacial Landmark Digitization via Segmentation-guided Partially-joint Regression Forest Model and Multi-scale Statistical Features.  IEEE Trans. on Biomedical Engineering, 63(9): 1820-1829, 2016.


  1. Jun Zhang, Jimin Liang, Chunhui Zhang, and Heng Zhao. Scale Invariant Texture Representation based on Frequency Decomposition and Gradient OrientationPattern Recognition Letters, 51: 57-62, 2015.
  2. Chunhui Zhang, Jimin Liang, Jun Zhang, and Heng Zhao. A New Shape Prior Model with Rotation InvariancePattern Recognition Letters, 54: 82-88, 2015.


  1. Jun Zhang, Duofang Chen, Jimin Liang, Huadan Xue, Jing Lei, Qin Wang, Dongmei Chen, Ming Meng, Zhengyu Jin, and Jie Tian. Incorporating MRI Structural Information into Bioluminescence Tomography: System, Heterogeneous Reconstruction and In Vivo QuantificationBiomedical Optics Express, 5(6): 1861, 2014.
  2. Chunhui Zhang, Jun Zhang, Heng Zhao, and Jimin Liang. A Part-based Probabilistic Model for Object Detection with OcclusionPloS One,  9(1): e84624, 2014.


  1. Jun Zhang, Jimin Liang, and Heng Zhao. Local Energy Pattern for Texture Classification using Self-adaptive Quantization ThresholdsIEEE Trans. on Image Processing,  22(1): 31-42, 2013.
  2. Jun Zhang, Heng Zhao, and Jimin Liang. Continuous Rotation Invariant Local Descriptors for Texton Dictionary-based Texture Classification. Computer Vision and Image Understanding, 117(1): 56-75, 2013.