Dr Luping Zhou (周泸萍) is an Associate Professor and Director of Software Engineering in School of Electrical and Computer Engineering (ECE), the University of Sydney. Dr Zhou obtained her PhD from Australian National University. Before she started her PhD, she was a senior research engineer, developing medical imaging applications for surgical navigation and planning through virtual/augmented reality systems. Dr Zhou was a recipient of ARC (Australian Research Council) DECRA award (Discovery Early Career Researcher Award). She has a 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, as well as general visual recognition problems. Dr Zhou is co-leading (since 2021) the Digital Health Imaging (DHI) research theme under Digital Sciences Initiatives (DSI) launched by the Faculty of Engineering, USyd. DHI is devoted to promoting the synergy of USyd’s strengths and leadership in AI and medical imaging and building trust with the industry for impactful research. Zhou is also affilated with the Sydney Artificial Intelligence Centre and the Brain and Mind Centre. Dr Zhou is a Senior member of IEEE.
News
1. Congratulations to Tong's paper about surgical endoscopic image low-light enhancement, receiving the MICCAI'24 Best Paper Honorable Mention award!
2. Zhou serves CVPR2025 as an Area Chair.
3. Zhou serves ICLR2025 as an Area Chair.
4. Zhou received the Dean's Award 2023 for "Emerging Leadership in Research" by the Faculty of Engineering, USyd.
5. We are organizing the First International Workshop on Vision-Language Models for Biomedical Applications: VLM4Bio 2024 in conjunction with ACM Multimedia 2024.
6. We have recently systematically evaluated GPT-4V’s capabilities across diverse medical imaging tasks, including Radiology Report Generation, Medical Visual Question Answering (VQA), and Medical Visual Grounding. Our paper can be found here: https://arxiv.org/pdf/2310.20381.pdf. While prior efforts have explored GPT-4V’s performance in medical image analysis, our study represents the first quantitative evaluation on publicly available benchmarks to the best of our knowledge. Three evaluation ways were included: quantitative analysis, human evaluation, and case study.
7. Zhou serves MICCAI2024 as an Area Chair.
8. Zhou serves ECCV2024 as an Area Chair.
9. Zhou joined the editorial board of the ELSEVIER journal Medical Image Analysis (MedIA).
10. We published our first attempt, R2GenGPT, which integrates Large Language Models (LLMs) for medical report generation, in Meta-Radiology. R2GenGPT attains SOTA performance by training only a lightweight visual alignment module with frozen LLM. The code has been available at https://github.com/wang-zhanyu/R2GenGPT. Updated: Google Deepmind's recent report cited our works of R2GenGPT and METransformer.
11. Zhou joined the editorial board of a new interdisciplinary journal Meta-Radiology. We are organising a special issue "LLM/ChatGPT/GPT-4 for Medical Imaging" with Meta-Radiology. The website of this special issue can be found here. In keeping with the rapid pace of AGI development, our review process will be efficient and acceptance decisions will be made promptly.
12. Zhou served AAAI2024 as a Senior Program Committee member (SPC).
13. We received funding from USyd-Fudan-BISA Flagship Research Program 2022 to support our research “Towards efficient and reliable AI with stochastic computation inspired by biological neural systems”.
14. We organised a MICCAI2022 workshop EPIMI for Ethical and Philosophical Issues in Medical Imaging.
15. We organised CVPR2022 workshop L3D-IVU, promoting interesting research related to learning with limited labelled data.
16. We successfully bid MICCAI2025 in Korea.
17. Zhou served AAAI2023 as a Senior Program Committee member (SPC).
18. Zhou served MICCAI2022 as an area chair, and a member of MICCAI Young Scientist Award (YSA) Committee.
19. Zhou gave a keynote talk at the workshop Computer Vision and Medical Computing (CVMC) hosted by ACCV (Asian Conference on Computer Vision) 2022, December 2022
20. Zhou gave an invited talk at IEEE EMBS Webinar Series of "Frontiers of Biomedical Imaging and Analysis" about automated medical report generation, March 2022.
21. Zhou succeeded in a Discovery Project (DP 2020-2022, leading CI) for image per-pixel prediction funded by Australian Research Council (ARC).
22. Our LesioLogic Project (led by Prof. Alistair McEwan) received research fund from MRFF 2020 Cardiovascular Health Mission.
23. Biting received EIS Best PhD Thesis award from Faculty of Engineering and Information Sciences in UoW. Congratulations!
24. Zhou gave a keynote talk at PRIME (Predictive Intelligence in Medicine), MICCAI 2021 about "Exploring Fine-grained Image-text Description for Diagnostic Captioning".
25. Zhou served MICCAI2021 and MICCAI2020 as an area chair; She co-chaired the oral session about medical image synthesis in MICCAI2021.
26. Zhou received USydney Thompson Equity Price 2021.
27. Zhou is recognised as IEEE TMI Distinguished Reviewer for her review work from 2018 through 2020.
28. Zhou serves Pattern Recognition (Elsevier) as an Associate Editor.
29. Zhou serves IEEE Trans. on Medical Imaging (IEEE TMI) as an Associate Editor.
30. Zhou serves Neurocomputing (Elsevier) as an Associate Editor.
31. Our work on Ea-GANs (Code) won the Dolby Scientific Paper Competition. Congratulations to Biting!
32. Our work about epilepsy prediction (led by Dr. Omid Kavehei) received Microsoft AI grant.
33. Zhou is recognized as the “Outstanding Reviewers” by ICCV’2019.
34. We are organizing the first international workshop on “Graph Learning in Medical Imaging” (GLMI), held together with MICCAI 2019, in Shenzhen, China. The proceedings could be found here.
35. Zhou gave an invited talk in Medical Image Computing Seminars (MICS) 2019, Suzhou, China, July 2019
36. Zhou gave an invited talk in Artificial Intelligence in Medicine Surgery and Healthcare (AMSAH) 2019, Sydney, Australia, March 2019
37. Our work about PET image prediction at low dose was reported by News of University of Sydney.
38. Zhou gave an invited talk in First Conference of Chinese Medical Imaging AI, Shanghai, China, December 2018
39. We are organizing a special issue on “High Performance Computing in Bio-medical Informatics” (HPC-BMI) with Neuroinformatics (Springer). The online Call-for-Paper is available here. Paper submission period is May 1-31, 2017.
40. Our organising committee successfully bid MICCAI 2019 in Hong Kong (General Chairs: Prof Dinggang Shen @ UNC-CH and Prof Tianming Liu @ UGA; moved to Shenzhen, China).
41. We are organizing a special issue “Machine Learning in Medical Imaging” with Pattern Recognition (Elsevier). The online Call-for-Paper is available here. The submission is due on January 31, 2016, through the website here. Please select “SI:MLMI” for “Article Type” during the submission procedure.
42. Zhou received the early career award (DECRA 2016-2018, sole CI) from Australian Research Council (ARC) to support her research in learning network structures from neuroimaging data.
43. Zhou is recognized as MICCAI’15 Best Reviewers Runner-ups.
44. Update: the workshop MICCAI-MLMI’15 has been completed successfully. This year, we again attracted above 140 registrants. The proceedings are available online here.
45. We are organizing the 6th international workshop on Machine Learning in Medical Imaging (MLMI 2015), held together with MICCAI 2015, in Munich, Germany. MLMI focuses on advancing the cutting-edge machine learning techniques and their use in medical imaging. The last workshop, MLMI 2014 held in Boston USA, attracted around 100 attendees, and the proceedings could be found here.
46. Zhou gave an invited talk on BrainKDD2015, hosted by ACM SIGKDD in Sydney in 2015.
Selected Publications
(Full list is here)
L. Papa, P. Russo, I. Amerini, and L. Zhou*, "A Survey on Efficient Vision Transformers: Algorithms, Techniques, and Performance Benchmarking”, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2024
Y. Wang, Y. Luo, C. Zu, B Zhan, Z. Jiao, Xi Wu, J. Zhou*, D. Shen, and L. Zhou*， “3D Multi-Modality Transformer-GAN for High-quality PET Reconstruction”, Medical Image Analysis (MedIA), 2024
JD. Zhang, Z. Cui, L. Zhou*, Y. Sun, Z. Li*, Z. Liu, and D. Shen*, "Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification with Iterative Cycle-consistent Semi-supervised Learning", IEEE Transactions on Medical Imaging (IEEE T-MI), 2023 (*co-corresponding authors)
J. Zhang, L. Zhou, L. Wang, M. Liu, and D. Shen, "Diffusion Kernel Attention Network for Brain Disorder Classification", IEEE Transactions on Medical Imaging (IEEE T-MI), 2022 (accepted in April 2022)
Z. Wang, H. Han, L. Wang, X. Li, and L. Zhou*, "Automated Radiographic Report Generation Purely on Transformer: A Multi-criteria Supervised Approach", IEEE Transactions on Medical Imaging (IEEE T-MI), 2022 (accepted in April 2022, * corresponding author)
Z. Zhang, L. Wang, Y. Wang, L. Zhou, J. Zhang, and F. Wang, "Dataset-driven Unsupervised Object Discovery for Region-based Instance Image Retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2022
B. Yu, L. Zhou*, L. Wang*, W. Yang, M. Yang, P. Bourgeat, and J. Fripp, "SA-LuT-Nets: Learning Sample-adaptive Intensity Lookup Tables for Brain Tumor Segmentation", IEEE Transaction on Medical Imaging (IEEE T-MI), 2021 (*co-corresponding authors, accepted in January 2021)
J. Zhang, L. Wang, L. Zhou and W. Li, “Beyond Covariance: SICE and Kernel based Visual Feature Representation”, International Journal of Computer Vision (IJCV) 2020 (accepted in August, 2020)
R. Su, D. Xu, L. Zhou, and W. Ouyang, “Progressive Cross-stream Cooperation in Spatial and Temporal Domain for Action Localization,” IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2020 (accepted in May 2020)
J. Huang, L. Zhou, L. Wang, and D. Zhang, "Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis", IEEE Transactions on Medical Imaging (IEEE T-MI), 2020 (accepted in Feb 2020)
B. Yu, L. Zhou*, L. Wang*, Y. Shi, J. Fripp, and P. Bourgeat, "Sample-adaptive GANs: Linking Global and Local Mappings for Cross-modality MR Image Synthesis ", IEEE Transactions on Medical Imaging (IEEE T-MI), 2020 (*co-corresponding authors, accepted in Jan 2020)
B. Yu, L. Zhou*, L. Wang*, Y. Shi, J. Fripp, and P. Bourgeat, "Ea-GANs: Edge-aware Generative Adversarial Networks for Cross-modality MR Image Synthesis", IEEE Transactions on Medical Imaging (IEEE T-MI), 2019 (*co-corresponding authors, winning the Dolby Scientific Paper Competition in Jan 2020) [Code]
Y. Wang, L. Zhou*, B. Yu, L. Wang, C. Zu, D.S. Lalush, W. Lin, X. Wu, J. Zhou, and D. Shen* , "3D Auto-context-based Locality Adaptive Multi-modality GANs for PET Synthesis ", IEEE Transactions on Medical Imaging (IEEE T-MI), 2019 (*co-corresponding authors, accepted in November 2018)
L. Zhou, L. Wang, L. Liu, P. Ogunbona, and D. Shen, “Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data”, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2016.
L. Wang, L. Zhou, C. Shen, L. Liu and H. Liu, “A Hierarchical Word-merging Algorithm with Class Separability Measure”, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2013
L. Zhou, L. Wang, and C. Shen, “Feature Selection with Redundancy Constrained Class Separability”, IEEE Transactions on Neural Networks, 2010 (IEEE TNN)
L. Zhou, R. Hartley, L. Wang, P. Lieby, and N. Barnes, "Identifying Anatomical Shape Difference by Regularized Discriminative Direction", IEEE Transactions on Medical Imaging (IEEE T-MI), 28(6), June 2009, pp937-950
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Z. Chen, L. Zhou, Z. Hu, and D. Xu, "Group-aware Parameter-efficient Updating for Content-Adaptive Neural Video Compression", ACM Multimedia (MM), Melbourne, Australia, 2024
Z. Wang, L. Wang*, Z. Zhao, M. Wu, C. Lyu, H., Li, D. Cai, L. Zhou*, S. Shi, and Z. Tu*, "Gpt4video: A Unified Multimodal Large Language Model for Instruction-followed Understanding and Safety-aware Generation", ACM Multimedia (MM), Melbourne, Australia, 2024 （Oral Presentation)
Z. Zhao, ZC. Wang, L. Wang, Y. Yuan, and L. Zhou*, "Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation", European Conference on Computer Vision (ECCV), Milano, Italy, 2024
ZC Wang, Z. Zhao, Y. Wu, L. Zhou*, and D. Xu*, "Progressive Target-Styled Feature Augmentation for Unsupervised Domain Adaptation on Point Clouds", European Conference on Computer Vision (ECCV), Milano, Italy, 2024
Y. Li, Z. Wang, Y. Liu, L. Wang, L. Liu, and L. Zhou*, “KARGEN: Knowledge-enhanced Automated Radiology Report Generation Using Large Language Models” International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Morocco, 2024 (Early Accept)
Y. Liu, Z. Wang, Y. Li, X. Liang, L. Liu, L. Wang, and L. Zhou*, “MRScore: Evaluating Radiology Report Generation with LLM-based Reward System” International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Morocco, 2024 (Early Accept)
T. Chen, Q. Lyu, L. Bai, E. Guo, H. Gao, X. Yang, H. Ren, and L. Zhou*, "LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Morocco, 2024 (Early Accept)
Z. Fu, K. Song, L. Zhou, and Y. Yang, "Noise-Aware Image Captioning with Progressively Exploring Mismatched Words", Thirty-Eight AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 2024
X. Liu, Z Chen, L. Zhou, D. Xu, W. Xi, G. Bai, Y. Zhao, and J. Zhao, "UFDA: Universal Federated Domain Adaptation with Practical Assumptions", Thirty-Eight AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 2024
Y. Duan, Z. Zhao，L. Qi, L. Zhou, L. Wang, and Y. Shi, "Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning", Thirty-Eight AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 202
Z. Zhao, M. Zhao, Y. Liu, D. Yin, and L. Zhou*, "Entropy-based Optimization on Individual and Global Predictions for Semi-Supervised Learning", ACM Multimedia (MM), Canada, 2023
Z. Chen, L. Relic, R. Azevedo, Y. Zhang, M. Gross, D. Xu, L. Zhou, and C. Schroers, "Neural Video Compression with Spatio-Temporal Cross-Covariance Transformers", ACM Multimedia (MM), Canada, 2023
B. P. Voutharoja, L. Wang, and L. Zhou, "Automatic Radiology Report Generation by Learning with Increasingly Hard Negatives", European Conference on Artificial Intelligence (ECAI), Kraków, Poland, 2023 (oral presentation)
Z. Zhang, L. Wang, L. Zhou, and P. Koniusz, "Learning Spatial-context-aware Global Visual Feature Representation for Instance Image Retrieval", International Conference on Computer Vision (ICCV), Paris, 2023
G. Gui, Z. Zhao, L. Qi, L. Zhou, L. Wang, and Y. Shi, “Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning”, International Conference on Computer Vision (ICCV), Paris, 2023
Y. Duan, Z. Zhao, L. Qi, L. Zhou, L. Wang, and Y. Shi, "Class Transition Tracking Based Pseudo-Rectifying Guidance for Semi-supervised Learning with Non-random Missing Labels", International Conference on Computer Vision (ICCV), Paris, 2023
S. Long, Z. Zhao, J. Yuan, Z. Tan, J. Liu, L. Zhou, S. Wang, and J. Wang, "Task-Oriented Multi-Modal Mutual Leaning for Vision-Language Models", International Conference on Computer Vision (ICCV), Paris, 2023
Z. Han, Y. Wang, L. Zhou, P. Wang, B. Yan, J. Zhou, Y. Wang, and D. Shen, "Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Canada, 2023 (Early Accept)
Z. Wang, L. Liu, L. Wang and L. Zhou*, “METransformer: Radiology Report Generation by Transformer with Multiple Expert Learners,” The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
X. Yang, G. Lin, Z. Chen, and L. Zhou*, "Neural Vector Fields: Implicit Representation by Explicit Learning", The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
Z. Zhao, S. Long, J. Pi, J. Wang, and L. Zhou*, "Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation", The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
Z. Zhao, L. Yang, S. Long, J. Pi, L. Zhou, and J. Wang, "Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation", The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
ZC. Wang, Z. Zhao, X. Xing, D. Xu, X. Kong, and L. Zhou*, "Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation", The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
S. Rahman, P. Koniusz, L. Wang, L. Zhou, P. Moghadam and C. Sun, "Learning Partial Correlation based Deep Visual Representation for Image Classification," The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
Y. Liu, Z. Wang, D. Xu, and L. Zhou*, "Q2ATransformer: Improving Medical VQA by an Answer Querying Decoder", Information Processing in Medical Imaging (IPMI), 2023 (Oral Presentation, *corresponding author)
G. Guan, Z. Zhao, L. Qi, L. Zhou, L. Wang, and Y. Shi, "Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class", Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022
Y. Duan, L. Qi, L. Wang, L. Zhou, Y. Shi, "RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning", European Conference on Computer Vision (ECCV), Tel-Aviv, Israel, 2022
Z. Wang, M. Tang, L. Wang, X. Li, and L. Zhou*, "A Medical Semantic-Assisted Transformer for Radiographic Report Generation", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Singapore, 2022 (* corresponding author)
P. Zeng, L. Zhou, C. Zu, X. Zeng, Z. Jiao, X. Wu, J. Zhou, D. Shen, and Y. Wang*, "3D CVT-GAN: A 3D Convolutional Vision Transformer-GAN for PET Reconstruction", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Singapore, 2022
Z. Zhao, L. Zhou*, Y. Duan, L. Wang, L. Qi, and Y. Shi*, "DC-SSL: Addressing Mismatched Class Distribution in Semi-supervised Learning", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (accepted)
Z. Zhao, L. Zhou*, L. Wang, Y. Shi*, and Y. Gao, "LaSSL: Label-guided Self-training for Semi-supervised Learning", Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 2022 (Oral Presentation, * co-corresponding authors) [code]
Z. Wang, L. Zhou*, L. Wang, and X. Li, "A Self-boosting Framework for Automated Radiographic Report Generation", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (corresponding author)
Y. Luo, Y. Wang*, C. Zu, B. Zhan, X. Wu, J. Zhou, D. Shen*, and L. Zhou*, "3D Transformer-GAN for High-quality PET Reconstruction", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Strasburg, France, 2021 (* co-corresponding authors)
K. Wang, B. Zhan, C. Zu, X. Wu, J. Zhou, L. Zhou, and Y. Wang, "Tripled-uncertainty Guided Mean Teacher Model for Semi-supervised Medical Image Segmentation", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Strasburg, France, 2021
S. Huang, W. Yang, L. Wang, L. Zhou, and M. Yang, "Few-shot Unsupervised Domain Adaptation with Image-to-Class Sparse Similarity Encoding", ACM International Conference on Multimedia (ACM MM), 2021
S. Rahman, L. Wang, C. Sun, and L. Zhou, "ReDro: Efficiently Learning Large-sized SPD Visual Representation", European Conference on Computer Vision (ECCV), Glasgow, UK, 2020
B. Yu, L. Zhou*, L. Wang*, W. Yang, M. Yang, P. Bourgeat, and J. Fripp, "Learning Sample-adaptive Intensity Lookup Table for Brain Tumor Segmentation", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Lima, Peru, 2020 (* co-corresponding authors)
K. Tian, C. Lin, M. Sun, L. Zhou, J. Yan, and W. Ouyang, "Improving Auto-Augment via Augmentation-Wise Weight Sharing", Annual Conference on Neural Information Processing Systems (NeurlPS), 2020
H. Wang, L. Zhou, and L. Wang, "Missed Detection vs False Alarm: Adversarial Learning for Small Object Segmentation", International Conference on Computer Vision (ICCV), Seoul, Korea, 2019 [Code]
L. Qi, L. Wang, J. Huo, L. Zhou, Y. Shi. and Y. Gao, "A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-ID", International Conference on Computer Vision (ICCV), Seoul, Korea, 2019
J. Huang, L. Zhou, L. Wang, and D. Zhang,"Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI) , Shenzhen, China, 2019 (early accept)
R. Su, W. Ouyang, L. Zhou, and D. Xu, "Improving Action Localization by Progressive Cross-stream Cooperation", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019
M. Engin, L. Wang, L. Zhou, and X. Liu, "DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition", European Conference on Computer Vision (ECCV), Munich, Germany, 2018
Y. Wang, L. Zhou*, L. Wang, B. Yu, C. Zu, D.S. Lalush, W. Lin, X. Wu, J. Zhou, and D. Shen*, "Locality Adaptive Multi-modality GANs for High-quality PET Image Synthesis", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI) , Granada, Spain, 2018 (*co-corresponding authors)
L. Zhou, L. Wang, J. Zhang, Y. Shi and Y. Gao, "Revisiting Distance Metric Learning for SPD Matrix based Visual Representation", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, 2017
L. Wang, J. Zhang, L. Zhou, C. Tang, and W. Li, “Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices”, International Conference on Computer Vision (ICCV), Santiago, Chile, 2015
L. Zhou, L. Wang, and P. Ogunbona, “Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Ohio, USA, 2014
L. Zhou, L. Wang, L. Liu, P. Ogunbona, and D. Shen, “Max-margin Based Learning for Discriminative Bayesian Network from Neuroimaging Data”, International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Boston, USA, 2014
L. Zhou, L. Wang, L. Liu, P. Ogunbona, and D. Shen, “Discriminative Brain Effective Connectivity Analysis for Alzheimers Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Oregon, USA, 2013
L. Wang, J. Zhang, L. Zhou, and W. Li, “A Fast Approximate AIB Algorithm for Distributional Word Clustering”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Oregon, USA, 2013
L. Zhou, O. Salvado, V. Dore, P. Bourgeat, P. Raniga, V. L. Villemagne, C. C. Rowe, and J. Fripp, “MR-less Surface-based Amyloid Estimation by Subject-specific Atlas Selection and Bayesian Fusion”, In Proc. International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), France, October 2012
L. Zhou, Y. Wang, Y. Li, P.T. Yap, and D. Shen, “Hierarchical Anatomical Brain Networks for MCI Prediction by Partial Least Square Analysis”, In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 11), Colorado Springs, USA, June 2011
L. Zhou, L. Wang, C. Shen, and N. Barnes, “Hippocampal Shape Classification Using Redundancy Constrained Feature Selection”, In Proc. International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), China, September 2010 (MICCAI Travel Award)
L. Zhou, R. Hartley, L. Wang, P. Lieby, N. Barnes, “Regularized Discriminative Direction for Shape Analysis”, In Proc. International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), USA, September 2008, pp628-635
L. Wang, L. Zhou, and C. Shen, "A Fast Algorithm for Creating A Compact and Discriminative Visual Codebook", 10th European Conference on Computer Vision (ECCV), France, October 2008, pp719-732
L. Zhou, R. Hartley, P. Lieby, N. Barnes, K. Anstey, N. Cherbuin and P. Sachdev, “A Study of Hippocampal Shape Difference Between Genders by Efficient Hypothesis Test and Discriminative Deformation”, In Proc. International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI) Australia, September 2007, pp375-383