Dr Luping Zhou (周泸萍) is an Associate Professor and a previous 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 (UoW), 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 was a recipient of ARC (Australian Research Council) DECRA award (Discovery Early Career Researcher Award) in 2015, and UoW Vice Chancellor Research Fellowship in 2012. 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, as well as general visual recognition problems.

News

1. We are organising a MICCAI2022 workshop EPIMI for Ethical and Philosophical Issues in Medical Imaging.

2. We are organising a CVPR2022 workshop L3D-IVU, promoting interesting research related to learning with limited labelled data.

3. We successfully bid MICCAI2025 in Korea.

3. Zhou served MICCAI2022 as an area chair.

4. Zhou gave an invited talk at IEEE EMBS Webinar Series of "Frontiers of Biomedical Imaging and Analysis" about automated medical report generation, March 2022.

5. Zhou succeeded in a Discovery Project (DP 2020-2022, leading CI) for image per-pixel prediction funded by Australian Research Council (ARC).

6. Our LesioLogic Project (led by Prof. Alistair McEwan) received research fund from MRFF 2020 Cardiovascular Health Mission.

7. Biting received EIS Best PhD Thesis award from Faculty of Engineering and Information Sciences in UoW. Congratulations!

8. Zhou gave a keynote talk at PRIME (Predictive Intelligence in Medicine), MICCAI 2021 about "Exploring Fine-grained Image-text Description for Diagnostic Captioning".

9. Zhou served MICCAI2021 and MICCAI2020 as an area chair; She co-chaired the oral session about medical image synthesis in MICCAI2021.

10. Zhou received USydney Thompson Equity Price 2021.

11. Zhou is recognised as IEEE TMI Distinguished Reviewer for her review work from 2018 through 2020.

12. Zhou serves Pattern Recognition (Elsevier) as an Associate Editor.

13. Zhou serves IEEE Trans. on Medical Imaging (IEEE TMI) as an Associate Editor.

14. Zhou serves Neurocomputing (Elsevier) as an Associate Editor.

15. Our work on Ea-GANs (Code) won the Dolby Scientific Paper Competition. Congratulations to Biting!

16. Our work about epilepsy prediction (led by Dr. Omid Kavehei) received Microsoft AI grant.

17. Zhou is recognized as the “Outstanding Reviewers” by ICCV’2019.

18. 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.

19. Zhou gave an invited talk in Medical Image Computing Seminars (MICS) 2019, Suzhou, China, July 2019

20. Zhou gave an invited talk in Artificial Intelligence in Medicine Surgery and Healthcare (AMSAH) 2019, Sydney, Australia, March 2019

21. Our work about PET image prediction at low dose was reported by News of University of Sydney.

22. Zhou gave an invited talk in First Conference of Chinese Medical Imaging AI, Shanghai, China, December 2018

23. 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.

24. 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).

25. 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.

26. 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.

27. Zhou is recognized as MICCAI’15 Best Reviewers Runner-ups.

28. Update: the workshop MICCAI-MLMI’15 has been completed successfully. This year, we again attracted above 140 registrants. The proceedings are available online here.

29. 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.

30. Zhou gave an invited talk on BrainKDD2015, hosted by ACM SIGKDD in Sydney in 2015.

Selected Publications

(Full list is here)

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. 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

P. Zeng, L. Zhou, C. Zu, X. Zeng, Z. Jiao, X. Wu, J. Zhou, 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)

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