(Full list is here)
Z. Wang, L. Wang, X. Li, and L. Zhou*, "Diagnostic Captioning by Cooperative Task Interactions and Sample-graph Consistency", IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2025
X. Yang, G. Lin, Z. Chen, and L. Zhou*, "Neural Vector Fields: Generalizing Distance Vector Fields by Codebooks and Zero-Curl Regularization", IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2025
L. Yang, Z. Chen*, K. Wang, and L. Zhou*, "Improving CXR Bone Suppression by Exploiting Domain-level and Instance-level Information", IEEE Transactions on Medical Imaging (IEEE-TMI), 2025
E. Guo, Z.C. Wang, Z. Zhao, and L. Zhou*, "Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels", IEEE Transactions on Medical Imaging (IEEE-TMI), 2025
ZL. Chen, Y. Li, Z. Wang, P. Gao, J. Barthelemy, L. Zhou, and L. Wang, "Enhancing Radiology Report Generation via Multi-Phased Supervision", IEEE Transactions on Medical Imaging (IEEE-TMI), 2025 (accepted)
Z. Cheng, J. Guo, J. Zhang, L. Qi, L. Zhou, Y. Shi, and Y. Gao, "Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation", IEEE Transactions on Medical Imaging (IEEE-TMI), 2025
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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T. Han, W. Xu, J. Gong, X. Yue, S. Guo, L. Zhou, and L. Bai, "InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis", International Conference on Computer Vision (ICCV), Hawai'i, 2025
T. Chen, S. Yang, J. Wang, Long Bai, H. Ren, and L. Zhou*, "Object-Aware Diffusion Model for Controllable Surgical Video Generation", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Korea, 2025
Q. Lyu, Y. Wang, T. Chen, E. Guo, and L. Zhou*, "WiD-PET: PET Image Reconstruction from Low-Dose Data Using a Wavelet-Informed Diffusion Model with Fast Inference", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Korea, 2025
J. Cui, L. Wen, Y. Fei, B. Liu, L. Zhou, D. Shen, and Y. Wang, "HiLa: Hierarchical Vision-Language Collaboration for Cancer Survival Prediction", International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Korea, 2025
E. Guo, Z. Zhao, ZC. Wang, T. Chen, Y. Liu, and L. Zhou*, "DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels", The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
W. Xu, V. Ila, L. Zhou, and C.T. Jin, "TB-HSU: Hierarchical 3D Scene Understanding with Contextual Affordances", Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI), Philadelphia, USA, 2025
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, Best Paper Nomination)
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, Best Paper Honorable Mention)
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, 2024
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) [Code]
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