Dr Luping Zhou

PhD in Computer Science (ANU)
Senior Lecturer
ARC DECRA Fellow
School of Electrical and Information Engineering

J03 - Electrical Engineering Building
The University of Sydney

Telephone +61 2 8627 6802

Website School of Electrical and Information Engineering

Software engineering

Biographical details

Dr Luping Zhou is a Senior Lecturer and ARC DECRA Fellow in the School of Electrical and Information Engineering at the University of Sydney, Australia. Prior to this, she was a Senior Lecturer at the University of Wollongong, where she maintains an honorary position. Dr Zhou obtained her PhD, MSc, and BEng from Australian National University, National University of Singapore and Southeast University, China, respectively. Upon completing her PhD, she worked as a postdoctoral research fellow at the University of North Carolina, Chapel Hill, USA and then as a research scientist in the Australian e-Health Research Centre, CSIRO. Dr Zhou is a recipient of ARC DECRA (Discovery Early Career Researcher Award) award in 2015. Before she started her Ph.D, Dr Zhou was a senior research engineer, developing medical imaging applications for surgical navigation and planning through virtual/augmented reality systems. Dr Zhou has broad research interest in medical image analysis, machine learning and computer vision. Her current research is focused on medical image analysis with statistical graphical models and deep learning.

Research interests

The advent of medical imaging techniques such as CT and MRI scans has significantly reshaped modern medical research and practice, transforming hospital diagnostic and treatment protocols. Dr Luping Zhou's research in computer-assisted medical image analysis aims to enhance the efficacy of these tools along with associated patient outcomes.

"Computer-assisted automatic processing and analysis of medical images is in high demand, due to its better precision, repeatability and objectivity for diagnosis. The associated research is known as medical image analysis.

"One of my current research focuses is to improve the early prediction of mental diseases, such as Alzheimer's disease (which is the second-leading cause of death in Australia), using imaging-based bio-markers automatically identified from brain images.

"In general my research focuses on significant problems that are of both scientific and commercial interests, such as the early diagnosis of multiple mental disorders (including Alzheimer's disease, attention-deficit/hyperactivity disorder and schizophrenia), image-guided radiation therapy for prostate cancer, automatic cell image classification (for the diagnosis of some autoimmune diseases), and brain tumour segmentation (for brain cancer staging).

"Medical image analysis is a good combination of my interests in computer science and in helping people to live healthy lives. My first job was in a company that built virtual reality systems for surgical training and planning. I found that was exactly what I want to do in my life, so I went back to university to complete a PhD in that area.

"My ultimate research goal is to automate medical image analysis with artificial intelligence, resulting in automatically generated radiology and other medical imaging reports.

"I've now been working in the field of medical image analysis for more than 12 years, in both industry and academia, and I joined the University of Sydney in 2017. The school here has a strong team in image analysis and biomedical engineering, and a long history of collaboration with hospitals. These factors will greatly benefit my future research."

Teaching and supervision

ELEC3802 - Fundamentals of Biomedical Engineering
ELEC3803 - Bioelectronics

Current research students

Project title Research student
Image and signal analysis towards scalable monitoring of brain disease progression and effective treatment Raquib-ul ALAM
Towards ultra-low power seizure forecasting systems Nhan TRUONG

Associations

IEEE Senior Member

Awards and honours

  • Discovery Early Career Researcher Award (DECRA) 2016-2018, Australian Research Council (ARC), Australia
  • MICCAI Best Reviewers Runner-ups, MICCAI 2015, Munich, Germany
  • Vice Chancellor Research Fellowship 2012-2014, University of Wollongong, Australia
  • ICT Centre Division Awards 2011 - Supporting Science Award (Commendation), 2011, CSIRO, Australia

Patents

2006

  • Frontier Advancing Polygonization, W.K. Leow, Z. Huang, L. Zhou and I. Atmosukarto, US7091969, granted on 15 Aug 2006, Licensed to a Singapore Company in 2010 with Royalty. Assignee: National University of Singapore
  • Systems and methods for automated measurements and visualization using knowledge structure mapping, L. Zhou, Y. Wang, and L. C. Goh, WIPO patent application WO/2006/056613, US patent application No.11/289230, Assignee: Bracco Group

2007

  • Systems and Methods for Collaborative Interactive Visualization of 3D Data Sets over a Network, L. Zhou, L. Serra, and L. C. Goh, WIPO patent application WO/2007/108776, US patent application No. 11/649425, Assignee: Bracco Group

2009

  • Shape Discrimination, R. Hartley, and L. Zhou, WIPO patent application WO/2009/052581, Assignee: National ICT, Australia

2011

  • MRI-independent amyloid assessment, L. Zhou, J. Fripp, O. Salvado, etc. Application filed Dec. 2011, Assignee: CSIRO, Australia
  • Learning-based Prostate Localization for Image Guided Radiation Therapy, S. Liao, D. Shen and L. Zhou, Application filed 2011, Assignee: University of North Carolina at Chapel Hill, USA

Selected grants

2017

  • Learning Network Structures from Neuroimages for Diagnosing Brain Diseases; Zhou L; Australian Research Council (ARC)/Discovery Early Career Researcher Award (DECRA).

Selected publications

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Book Chapters

  • Wang, L., Liu, L., Zhou, L., Caan, K. (2014). Application of SVMs to the Bag-of-Features Model: A Kernel Perspective. In Yunqian Ma, Guodong Guo (Eds.), Support Vector Machines Applications, (pp. 155-189). Heidelberg: Springer, Cham. [More Information]
  • Shen, D., Wee, C., Zhang, D., Zhou, L., Yap, P. (2014). Machine learning techniques for AD/MCI diagnosis and prognosis. In Sumeet Dua, U Rajendra Acharya, Prerna Dua (Eds.), Machine Learning in Healthcare Informatics, (pp. 147-179). Berlin: Springer. [More Information]
  • Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D. (2014). Support vector machines for neuroimage analysis: Interpretation from discrimination. In Yunqian Ma, Guodong Guo (Eds.), Support Vector Machines Applications, (pp. 191-220). Heidelberg: Springer, Cham. [More Information]

Journals

  • Wang, Y., Zhou, L., Yu, B., Wang, L., Zu, C., Lalush, D., Lin, W., Wu, X., Zhou, J., Shen, D. (2018). 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Transactions on Medical Imaging, In Press. [More Information]
  • Wang, Y., Yu, B., Wang, L., Zu, C., Lalush, D., Lin, W., Wu, X., Zhou, J., Shen, D., Zhou, L. (2018). 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. NeuroImage, 174, 550-562. [More Information]
  • Gao, Z., Wang, L., Zhou, L. (2018). A Probabilistic Approach to Cross-region Matching based Image Retrieval. IEEE Transactions on Image Processing, 28(3), 1191-1204. [More Information]
  • Li, W., Gao, Y., Wang, L., Zhou, L., Huo, J., Shi, Y. (2018). OPML: A one-pass closed-form solution for online metric learning. Pattern Recognition, 75, 1339-1351. [More Information]
  • An, H., Zhou, L., Yu, Y., Fan, H., Fan, F., Tan, S., Wang, Z., Boz, Z., Shi, J., Yang, F., et al (2018). Serum NCAM levels and cognitive deficits in first episode schizophrenia patients versus health controls. Schizophrenia Research, 192, 457-458. [More Information]
  • Wang, L., Liu, L., Zhou, L. (2017). A graph-embedding approach to hierarchical visual word mergence. IEEE Transactions on Neural Networks and Learning Systems, 28(2), 308-320. [More Information]
  • Gao, Z., Wang, L., Zhou, L., Zhang, J. (2017). HEp-2 Cell Image Classification with Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 21(2), 416-428. [More Information]
  • Ni, H., Qin, J., Zhou, L., Zhao, Z., Wang, J., Hou, F. (2017). Network analysis in detection of early-stage mild cognitive impairment. Physica A, 478, 113-119. [More Information]
  • Zhang, J., Zhou, L., Wang, L. (2017). Subject-adaptive Integration of Multiple SICE Brain Networks with Different Sparsity. Pattern Recognition, 63, 642-652. [More Information]
  • Ni, H., Zhou, L., Ning, X., Wang, L. (2016). Exploring multifractal-based features for mild Alzheimer's disease classification. Magnetic Resonance in Medicine, 76(1), 259-269. [More Information]
  • Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D. (2016). Learning discriminative Bayesian networks from high-dimensional continuous neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2269-2283. [More Information]
  • Zhang, J., Wang, L., Zhou, L., Li, W. (2016). Learning Discriminative Stein Kernel for SPD Matrices and Its Applications. IEEE Transactions on Neural Networks and Learning Systems, 27(5), 1020-1033. [More Information]
  • Liu, X., Zhou, L., Wang, L., Zhang, J., Yin, J., Shen, D. (2015). An efficient radius-incorporated MKL algorithm for Alzheimer's disease prediction. Pattern Recognition, 48(7), 2141-2150. [More Information]
  • Zhang, J., Zhou, L., Wang, L., Li, W. (2015). Functional brain network classification with compact representation of SICE matrices. IEEE Transactions on Biomedical Engineering, 62(6), 1623-1634. [More Information]
  • Nin, H., Zhou, L., Zeng, P., Huang, X., Liu, H., Ning, X. (2015). Multifractal analysis of white matter structural changes on 3D magnetic resonance imaging between normal aging and early Alzheimer's disease. Chinese Physics B, 24(7), 1-7. [More Information]
  • Wang, L., Zhou, L., Shen, C., Liu, L., Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 417-435. [More Information]
  • Zhou, L., Salvado, O., Dore, V., Bourgeat, P., Raniga, P., Macaulay, S., Ames, D., Masters, C., Ellis, K., Villemagne, V., et al (2014). MR-Less Surface-Based Amyloid Assessment Based on 11C PiB PET. PloS One, 9(1), 1-14. [More Information]
  • Dore, V., Villemagne, V., Bourgeat, P., Fripp, J., Acosta, O., Chetelat, G., Zhou, L., Martins, R., Ellis, K., et al (2013). Cross-sectional and longitudinal analysis of the relationship between a? deposition, cortical thickness, and memory in cognitively unimpaired individuals and in alzheimer disease. JAMA Neurology, 70(7), 903-911. [More Information]
  • Li, Y., Wang, Y., Wu, G., Shi, F., Zhou, L., Lin, W., Shen, D. (2012). Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features. Neurobiology of Aging, 33(2), 427.e15-427.e30. [More Information]

Edited Journals

  • Zhou, L., Rekik, I., Yan, C., Wu, G. (2018). Special Issue on High Performance Computing in Bio-medical Informatics. Neuroinformatics, 16(3-4). [More Information]

Conferences

  • Yu, B., Zhou, L., Wang, L., Fripp, J., Bourgeat, P. (2018). 3D cGAN based cross-modality MR image synthesis for brain tumor segmentation. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Tian, P., Qi, L., Shi, Y., Zhou, L., Gao, Y., Sheri, D. (2018). A Novel Image-Specific Transfer Approach for Prostate Segmentation in MR Images. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wu, J., Zhou, L., Cai, C., Shen, J., Lau, S., Yong, J. (2018). Data Fusion for MaaS: Opportunities and Challenges. 22nd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018, Piscataway: IEEE Computer Society. [More Information]
  • Engin, M., Wang, L., Zhou, L., Liu, X. (2018). DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition. 15th European Conference on Computer Vision (ECCV 2018), Cham: Springer. [More Information]
  • Zhang, Z., Wang, L., Wang, Y., Zhou, L., Zhang, J., Chen, F. (2018). Instance image retrieval by aggregating sample-based discriminative characteristics. 8th ACM International Conference on Multimedia Retrieval, ICMR 2018, New York: ACM New York, NY, USA. [More Information]
  • Wang, Y., Zhou, L., Wang, L., Yu, B., Zu, C., Lalush, D., Lin, W., Wu, X., Zhou, J., Shen, D. (2018). Locality adaptive multi-modality GANs for high-quality PET image synthesis. 21st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018), Cham: Springer. [More Information]
  • Zhao, Y., Wang, L., Zhou, L., Shi, Y., Gao, Y. (2018). Modelling Diffusion Process by Deep Neural Networks for Image Retrieval. 29th British Machine Vision Conference (BMVC 2018), United Kingdom: BMVC Organisers.
  • Zu, C., Wang, Y., Zhou, L., Wang, L., Zhang, D. (2018). Multi-modality feature selection with adaptive similarity learning for classification of Alzheimer's disease. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wang, Y., Yu, B., Wang, L., Zu, C., Luo, Y., Wu, X., Yang, Z., Zhou, J., Zhou, L. (2018). Tumor segmentation via multi-modality joint dictionary learning. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Gao, Z., Wang, L., Zhou, L., Yang, M. (2017). Infomax principle based pooling of deep convolutional activations for image retrieval. 2017 IEEE International Conference on Multimedia and Expo (ICME 2017), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhou, L., Wang, L., Zhang, J., Shi, Y., Gao, Y. (2017). Revisiting Distance Metric Learning for SPD Matrix based Visual Representation. 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Comor, I., Zhao, Y., Gao, Z., Zhou, L., Wang, L. (2016). Image Descriptors from ConvNets: Comparing Global Pooling Methods for Image Retrieval. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016), Gold Coast: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhao, Y., Wang, L., Gao, Z., Comor, I., Zhang, W., Zhou, L. (2016). Semi-Supervised Weight Learning for the Spatial Search Method in ConvNet-Based Image Retrieval. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016), Gold Coast: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wang, L., Zhang, J., Zhou, L., Tang, C., LI, W. (2015). Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices. 15th IEEE International Conference on Computer Vision (ICCV 2015), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhou, L., Wang, L., Wang, Q., Shi, Y. (2015). Machine learning in medical imaging: 6th International Workshop, MLMI 2015 held in conjunction with MICCAI 2015 Munich, Germany, October 5, 2015 proceedings. Machine learning in medical imaging: 6th International Workshop, Heidelberg: Springer Verlag. [More Information]
  • Zhou, L., Wang, L., Ogunbona, P. (2014). Discriminative sparse inverse covariance matrix: Application in brain functional network classification. 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhao, Y., Gao, Z., Wang, L., Zhou, L. (2014). Experimental study of unsupervised feature learning for HEp-2 cell images clustering. The International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014), Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhang, J., Zhou, L., Wang, L., LI, W. (2014). Exploring compact representation of SICE matrices for functional brain network classification. 5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Heidelberg: Springer, Cham.
  • Gao, Z., Zhang, J., Zhou, L., Wang, L. (2014). HEp-2 Cell Image Classification With Deep Convolutional Neural Networks. 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wu, G., Zhang, D., Zhou, L. (2014). Machine Learning in Medical Imaging: 5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014, Proceedings. 5th International Workshop, MLMI 2014, Switzerland: Springer.
  • Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D. (2014). Max-margin based learning for discriminative Bayesian network from neuroimaging data. 17th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, Cham, Switzerland: Springer.
  • Wang, L., Zhang, J., Zhou, L. (2013). A fast approximate AIB algorithm for distributional word clustering. IEEE Computer Society Conference on computer vision and Pattern Recognition 2013, Los Alamitos: IEEE Computer Society. [More Information]
  • Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D. (2013). Discriminative brain effective connectivity analysis for alzheimer's disease: A kernel learning approach upon sparse gaussian bayesian network. IEEE Computer Society Conference on computer vision and Pattern Recognition 2013, Los Alamitos: IEEE Computer Society. [More Information]
  • Zhou, L., Salvado, O., Dore, V., Bourgeat, P., Raniga, P., Villemagne, V., Rowe, C., Fripp, J. (2012). MR-less surface-based amyloid estimation by subject-specific atlas selection and Bayesian fusion. Medical Image Computing and Computer-Assisted Intervention � MICCAI 2012 15th International Conference, Heidelberg: Springer.

2018

  • Wang, Y., Zhou, L., Yu, B., Wang, L., Zu, C., Lalush, D., Lin, W., Wu, X., Zhou, J., Shen, D. (2018). 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Transactions on Medical Imaging, In Press. [More Information]
  • Yu, B., Zhou, L., Wang, L., Fripp, J., Bourgeat, P. (2018). 3D cGAN based cross-modality MR image synthesis for brain tumor segmentation. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wang, Y., Yu, B., Wang, L., Zu, C., Lalush, D., Lin, W., Wu, X., Zhou, J., Shen, D., Zhou, L. (2018). 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. NeuroImage, 174, 550-562. [More Information]
  • Tian, P., Qi, L., Shi, Y., Zhou, L., Gao, Y., Sheri, D. (2018). A Novel Image-Specific Transfer Approach for Prostate Segmentation in MR Images. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Gao, Z., Wang, L., Zhou, L. (2018). A Probabilistic Approach to Cross-region Matching based Image Retrieval. IEEE Transactions on Image Processing, 28(3), 1191-1204. [More Information]
  • Wu, J., Zhou, L., Cai, C., Shen, J., Lau, S., Yong, J. (2018). Data Fusion for MaaS: Opportunities and Challenges. 22nd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018, Piscataway: IEEE Computer Society. [More Information]
  • Engin, M., Wang, L., Zhou, L., Liu, X. (2018). DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition. 15th European Conference on Computer Vision (ECCV 2018), Cham: Springer. [More Information]
  • Zhang, Z., Wang, L., Wang, Y., Zhou, L., Zhang, J., Chen, F. (2018). Instance image retrieval by aggregating sample-based discriminative characteristics. 8th ACM International Conference on Multimedia Retrieval, ICMR 2018, New York: ACM New York, NY, USA. [More Information]
  • Wang, Y., Zhou, L., Wang, L., Yu, B., Zu, C., Lalush, D., Lin, W., Wu, X., Zhou, J., Shen, D. (2018). Locality adaptive multi-modality GANs for high-quality PET image synthesis. 21st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018), Cham: Springer. [More Information]
  • Zhao, Y., Wang, L., Zhou, L., Shi, Y., Gao, Y. (2018). Modelling Diffusion Process by Deep Neural Networks for Image Retrieval. 29th British Machine Vision Conference (BMVC 2018), United Kingdom: BMVC Organisers.
  • Zu, C., Wang, Y., Zhou, L., Wang, L., Zhang, D. (2018). Multi-modality feature selection with adaptive similarity learning for classification of Alzheimer's disease. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Li, W., Gao, Y., Wang, L., Zhou, L., Huo, J., Shi, Y. (2018). OPML: A one-pass closed-form solution for online metric learning. Pattern Recognition, 75, 1339-1351. [More Information]
  • An, H., Zhou, L., Yu, Y., Fan, H., Fan, F., Tan, S., Wang, Z., Boz, Z., Shi, J., Yang, F., et al (2018). Serum NCAM levels and cognitive deficits in first episode schizophrenia patients versus health controls. Schizophrenia Research, 192, 457-458. [More Information]
  • Zhou, L., Rekik, I., Yan, C., Wu, G. (2018). Special Issue on High Performance Computing in Bio-medical Informatics. Neuroinformatics, 16(3-4). [More Information]
  • Wang, Y., Yu, B., Wang, L., Zu, C., Luo, Y., Wu, X., Yang, Z., Zhou, J., Zhou, L. (2018). Tumor segmentation via multi-modality joint dictionary learning. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

2017

  • Wang, L., Liu, L., Zhou, L. (2017). A graph-embedding approach to hierarchical visual word mergence. IEEE Transactions on Neural Networks and Learning Systems, 28(2), 308-320. [More Information]
  • Gao, Z., Wang, L., Zhou, L., Zhang, J. (2017). HEp-2 Cell Image Classification with Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 21(2), 416-428. [More Information]
  • Gao, Z., Wang, L., Zhou, L., Yang, M. (2017). Infomax principle based pooling of deep convolutional activations for image retrieval. 2017 IEEE International Conference on Multimedia and Expo (ICME 2017), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Ni, H., Qin, J., Zhou, L., Zhao, Z., Wang, J., Hou, F. (2017). Network analysis in detection of early-stage mild cognitive impairment. Physica A, 478, 113-119. [More Information]
  • Zhou, L., Wang, L., Zhang, J., Shi, Y., Gao, Y. (2017). Revisiting Distance Metric Learning for SPD Matrix based Visual Representation. 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhang, J., Zhou, L., Wang, L. (2017). Subject-adaptive Integration of Multiple SICE Brain Networks with Different Sparsity. Pattern Recognition, 63, 642-652. [More Information]

2016

  • Ni, H., Zhou, L., Ning, X., Wang, L. (2016). Exploring multifractal-based features for mild Alzheimer's disease classification. Magnetic Resonance in Medicine, 76(1), 259-269. [More Information]
  • Comor, I., Zhao, Y., Gao, Z., Zhou, L., Wang, L. (2016). Image Descriptors from ConvNets: Comparing Global Pooling Methods for Image Retrieval. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016), Gold Coast: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D. (2016). Learning discriminative Bayesian networks from high-dimensional continuous neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2269-2283. [More Information]
  • Zhang, J., Wang, L., Zhou, L., Li, W. (2016). Learning Discriminative Stein Kernel for SPD Matrices and Its Applications. IEEE Transactions on Neural Networks and Learning Systems, 27(5), 1020-1033. [More Information]
  • Zhao, Y., Wang, L., Gao, Z., Comor, I., Zhang, W., Zhou, L. (2016). Semi-Supervised Weight Learning for the Spatial Search Method in ConvNet-Based Image Retrieval. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016), Gold Coast: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

2015

  • Liu, X., Zhou, L., Wang, L., Zhang, J., Yin, J., Shen, D. (2015). An efficient radius-incorporated MKL algorithm for Alzheimer's disease prediction. Pattern Recognition, 48(7), 2141-2150. [More Information]
  • Wang, L., Zhang, J., Zhou, L., Tang, C., LI, W. (2015). Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices. 15th IEEE International Conference on Computer Vision (ICCV 2015), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhang, J., Zhou, L., Wang, L., Li, W. (2015). Functional brain network classification with compact representation of SICE matrices. IEEE Transactions on Biomedical Engineering, 62(6), 1623-1634. [More Information]
  • Zhou, L., Wang, L., Wang, Q., Shi, Y. (2015). Machine learning in medical imaging: 6th International Workshop, MLMI 2015 held in conjunction with MICCAI 2015 Munich, Germany, October 5, 2015 proceedings. Machine learning in medical imaging: 6th International Workshop, Heidelberg: Springer Verlag. [More Information]
  • Nin, H., Zhou, L., Zeng, P., Huang, X., Liu, H., Ning, X. (2015). Multifractal analysis of white matter structural changes on 3D magnetic resonance imaging between normal aging and early Alzheimer's disease. Chinese Physics B, 24(7), 1-7. [More Information]

2014

  • Wang, L., Zhou, L., Shen, C., Liu, L., Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 417-435. [More Information]
  • Wang, L., Liu, L., Zhou, L., Caan, K. (2014). Application of SVMs to the Bag-of-Features Model: A Kernel Perspective. In Yunqian Ma, Guodong Guo (Eds.), Support Vector Machines Applications, (pp. 155-189). Heidelberg: Springer, Cham. [More Information]
  • Zhou, L., Wang, L., Ogunbona, P. (2014). Discriminative sparse inverse covariance matrix: Application in brain functional network classification. 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhao, Y., Gao, Z., Wang, L., Zhou, L. (2014). Experimental study of unsupervised feature learning for HEp-2 cell images clustering. The International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014), Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhang, J., Zhou, L., Wang, L., LI, W. (2014). Exploring compact representation of SICE matrices for functional brain network classification. 5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Heidelberg: Springer, Cham.
  • Gao, Z., Zhang, J., Zhou, L., Wang, L. (2014). HEp-2 Cell Image Classification With Deep Convolutional Neural Networks. 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wu, G., Zhang, D., Zhou, L. (2014). Machine Learning in Medical Imaging: 5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014, Proceedings. 5th International Workshop, MLMI 2014, Switzerland: Springer.
  • Shen, D., Wee, C., Zhang, D., Zhou, L., Yap, P. (2014). Machine learning techniques for AD/MCI diagnosis and prognosis. In Sumeet Dua, U Rajendra Acharya, Prerna Dua (Eds.), Machine Learning in Healthcare Informatics, (pp. 147-179). Berlin: Springer. [More Information]
  • Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D. (2014). Max-margin based learning for discriminative Bayesian network from neuroimaging data. 17th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, Cham, Switzerland: Springer.
  • Zhou, L., Salvado, O., Dore, V., Bourgeat, P., Raniga, P., Macaulay, S., Ames, D., Masters, C., Ellis, K., Villemagne, V., et al (2014). MR-Less Surface-Based Amyloid Assessment Based on 11C PiB PET. PloS One, 9(1), 1-14. [More Information]
  • Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D. (2014). Support vector machines for neuroimage analysis: Interpretation from discrimination. In Yunqian Ma, Guodong Guo (Eds.), Support Vector Machines Applications, (pp. 191-220). Heidelberg: Springer, Cham. [More Information]

2013

  • Wang, L., Zhang, J., Zhou, L. (2013). A fast approximate AIB algorithm for distributional word clustering. IEEE Computer Society Conference on computer vision and Pattern Recognition 2013, Los Alamitos: IEEE Computer Society. [More Information]
  • Dore, V., Villemagne, V., Bourgeat, P., Fripp, J., Acosta, O., Chetelat, G., Zhou, L., Martins, R., Ellis, K., et al (2013). Cross-sectional and longitudinal analysis of the relationship between a? deposition, cortical thickness, and memory in cognitively unimpaired individuals and in alzheimer disease. JAMA Neurology, 70(7), 903-911. [More Information]
  • Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D. (2013). Discriminative brain effective connectivity analysis for alzheimer's disease: A kernel learning approach upon sparse gaussian bayesian network. IEEE Computer Society Conference on computer vision and Pattern Recognition 2013, Los Alamitos: IEEE Computer Society. [More Information]

2012

  • Li, Y., Wang, Y., Wu, G., Shi, F., Zhou, L., Lin, W., Shen, D. (2012). Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features. Neurobiology of Aging, 33(2), 427.e15-427.e30. [More Information]
  • Zhou, L., Salvado, O., Dore, V., Bourgeat, P., Raniga, P., Villemagne, V., Rowe, C., Fripp, J. (2012). MR-less surface-based amyloid estimation by subject-specific atlas selection and Bayesian fusion. Medical Image Computing and Computer-Assisted Intervention � MICCAI 2012 15th International Conference, Heidelberg: Springer.

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