PUBLICATIONS

I have worked on the many diverse projects in medical imaging, computer vision, document image analysis, and natural language processing. Following are my publications.

Books

[2] Y. Zheng and D. Comaniciu, Marginal Space Learning for Medical Image Analysis: Efficient Detection and Segmentation of Anatomical Structures, Springer, 2014, (ISBN: 978-1-493-90599-7).

[1] Y. Zheng, D. Doermann, and H. Li, Handwritten Document Image Processing: Identification, Matching, and Indexing of Handwriting in Noisy Document Images, VDM Verlag Dr. Muller, 2008, (ISBN: 978-3-639-09192-2).

Books Edited

[1] L. Lu, Y. Zheng, G. Carneiro, and L. Yang, Deep Learning and Convolutional Neural Networks for Medical Image Computing, Springer, 2017.

Book Chapters

[7] Z. Zhang, L. Yang, and Y. Zheng, “Multimodal Medical Volumes Translation and Segmentation with Generative Adversarial Network,” Handbook of MICCAI, Edited by K. Zhou, Elsevier,2019, pp. 183-204.

[6] G. Carneiro, Y. Zheng, F. Xing, and L. Yang, “Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis,” Deep Learning and Convolutional Neural Networks for Medical Image Computing, Edited by L. Lu, Y. Zheng, G. Carneiro, and L. Yang, Springer,2017, pp. 11-32 [Download].

[5] Y. Zheng, D. Liu, B. Georgescu, H. Nguyen, and D. Comaniciu, “Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning,” Deep Learning and Convolutional Neural Networks for Medical Image Computing, Edited by L. Lu, Y. Zheng, G. Carneiro, and L. Yang, Springer,2017, pp. 49-61. [Download]

[4] Y. Zheng, D. Liu, B. Georgescu, D. Xu, and D. Comaniciu, “Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context,” Deep Learning and Convolutional Neural Networks for Medical Image Computing, Edited by L. Lu, Y. Zheng, G. Carneiro, and L. Yang, Springer,2017, pp. 241-255. [Download]

[3] Y. Zheng, “Model Based 3D Cardiac Image Segmentation With Marginal Space Learning,” Medical Image Recognition, Segmentation and Parsing: Methods, Theories and Applications, Edited by S. K. Zhou, Elsevier, 2015, pp. 383-404. [Download]

[2] S. K. Zhou, J. Zhang, and Y. Zheng, “Discriminative Learning for Detection and Segmentation of Anatomical Structures,” Ensemble Learning: Theory and Application, Edited by C. Zhang and Y. Ma, Springer, 2012, pp. 273-306, (ISBN: 978-1-4419-9325-0). [Download]

[1] Y. Wang, B. Georgescu, T. Chen, W. Wu, P. Wang, X. Lu, R. Ionasec, Y. Zheng, and D. Comaniciu, “Learning-based Detection and Tracking in Medical Imaging: A Robust Information-Fusion Approach,” Deformation Models: Tracking, Animation and Applications, Edited by Arnau Mir, Manuel Gonzalez, and Javier Varona, Springer, 2012, pp. 209-236, (ISBN: 978-9-4007-5445-4). [Download]

Journal Publications

[101] Y. Li，Y. Huang, N. He, K. Ma, and Y. Zheng, “Improving Vision Transformer for Medical Image Classification via Token-wise Perturbation,” Journal of Visual Communication and Image Representation, 2024.

[100] H. Wang, Y. Zhang, Y. Yang, Y. Zheng, and K.-F. Wong, “Acquiring New Knowledge Without Losing Old Ones for Effective Continual Dialogue Policy Learning,” IEEE Transactions on Knowledge and Data Engineering, 2024.

[99] W. Zhang, H. Liu, J. Xie, Y. Huang, Y. Li, R. Ramachandra, and Y. Zheng, “Anomaly Detection via Gating Highway Connection for Retinal Fundus Images,” Pattern Recognition, 2024.

[98] H. Yang, X. Wu, Z. Qiu, Y. Zheng, and X. Chen, “Distributional Fairness-aware Recommendation,” ACM Transactions on Information Systems, 2024.

[97] F. Yang, X. Li, H. Duan, F. Xu, Y. Huang, X. Zhang, Y. Long, and Y. Zheng, “MRL-Seg: Overcoming Imbalance in Medical Image Segmentation with Multi-Step Reinforcement Learning,” IEEE Journal of Biomedical and Health Informatics, 2024.

[96] Q. Yao, Z. He, Y. Li, K. Ma, Y. Zheng, and S. K. Zhou, “Adversarial Medical Image with Hierarchical Feature Hiding,” IEEE Transactions on Medical Imaging, 2024.

[95] Z. Cai, Y. Huang, T. Zhang, X.-Y. Jing, Y. Zheng, and L. Shao, “Attention Cycle-consistent Universal Network for More Universal Domain Adaptation,” Pattern Recognition, 2024.

[94] H. Liu, D. Wei, D. Lu, X. Tang, L. Wang, and Y. Zheng, “Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations,” Medical Image Analysis, 2023.

[93] Y. Zhang, Q. Yao, L. Yue, X. Wu, Z. Zhang, Z. Lin, and Y. Zheng, “Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network,” Nature Computational Science, 2023.

[92] Q. Bi, X. Sun, S. Yu, K. Ma, C. Bian, M. Ning, Y. Huang, Y. Li, H. Liu, Y. Zheng, and N. He, “MIL-ViT: A Multiple Instance Vision Transformer for Fundus Image Classification,” Journal of Visual Communication and Image Representation, 2023.

[91] G. Xie, J. Wang, Y. Huang, J. Lyu, F. Zheng, Y. Zheng, and Y. Jin, “Cross-Modality Neuroimage Synthesis: A Survey,” ACM Computing Surveys, 2023.

[90] F. Liu, T. Zhu, X. Wu, B. Yang, C. You, C. Wang, L. Lu, Z. Liu, Y. Zheng, X. Sun, Y. Yang, and D. A. Clifton, “A Multimodal Large Language Modelling Deep Learning Framework for the Future Pandemic,” npj Digital Medicine, 2023.

[89] Z. Wen, Y. Ye, J. Su, T. Li, J. Wan, S. Zheng, Z. Hong, S. He, H. Duan, Y. Li, Y. Huang and Y. Zheng, “Unraveling Complexity: An Exploration into the Large-Scale Multi-Modal Signal Processing,” IEEE Transactions on Intelligent Systems, 2023.

[88] H. Wang, Q. Xie, D. Zeng, J. Ma, D. Meng, and Y. Zheng, “OSCNet: Orientation-Shared Convolutional Network for CT Metal Artifact Learning,” IEEE Transactions on Medical Imaging, 2023.

[87] X. Miao, Y. Bai, H. Duan, Y. Huang, F. Wan, X. Xu, Y. Long, and Y. Zheng, “DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume,” IEEE Transactions on Circuits and Systems for Video Technology, 2023.

[86] Y. Lin, Z. Qu, H. Chen, Z. Gao, Y. Li, L. Xia, K. Ma, Y. Zheng, and K.-T. Cheng, “Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training,” Medical Image Analysis, 2023.

[85] D. Wei, Y. Huang, D. Lu, Y. Li, and Y. Zheng, “Automatic View Plane Prescription for Cardiac Magnetic Resonance Imaging via Supervision by Spatial Relationship between Views,” Medical Physics, 2023.

[84] Z. Xu, Y. Wang, D. Lu, X. Luo, J. Yan, Y. Zheng, and R. K. Tong, “Ambiguity-selective Consistency Regularization for Mean-Teacher Semi-supervised Medical Image Segmentation,” Medical Image Analysis, 2023.

[83] B. Liu, D. Lu, D. Wei, X. Wu, Y. Wang, Y. Zhang, and Y. Zheng, “Improving Medical Vision-Language Contrastive Pretraining with Semantics-aware Triage,” IEEE Transactions on Medical Imaging, 2023.

[82] M. Ning, D. Lu, Y. Xie, D. Chen, D. Wei, Y. Zheng, Y. Tian, S. Yan, and L. Yuan, “MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

[81] Y. Shi, H. Wang, H. Ji, H. Liu, Y. Li, N. He, D. Wei, Y. Huang, Q. Dai, J. Wu, X. Chen, Y. Zheng, and H. Yu, “A Deep Weakly Semi-Supervised Framework for Endoscopic Lesion Segmentation,” Medical Image Analysis, 2023.

[80] J. Wang, G. Xie, Y. Huang, J. Lyu, F. Zheng, Y. Zheng, and Y. Jin, “FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis,” NeuroComputing, 2023.

[79] J. Shi, L. Tang, Y. Li, X. Zhang, Z. Gao, Y. Zheng, C. Wang, T. Gong, and Chen Li, “A Structure-aware Hierarchical Graph-based Multiple Instance Learning Framework for pT Staging in Histopathological Image,” IEEE Transactions on Medical Imaging, 2023.

[78] M. Wang, Y. Shi, H. Yang, Z. Zhang, Z. Lin, and Y. Zheng, “Probing the Impacts of Visual Context in Multimodal Entity Alignment,” Data Science and Engineering, 2023.

[77] J. Zhang, R. Gu, P. Xue, H. Zheng, Y. Zheng, M. Liu, G. Wang, L. Ma, and L. Gu, “Explainable Continual Segmentation across Multiple Sites via Comprehensive Importance-based Selective Regularization,” IEEE Transactions on Medical Imaging, 2023.

[76] Z. Gao, A. Mao, K. Wu, Y. Li, L. Zhao, J. Wu, L. Yu, C. Xing, T. Gong, Y. Zheng, M. Zhou, C. Li, and X. Zhang, “Childhood Leukemia Classification via Information Bottleneck Enhanced Hierarchical Multi-Instance Learning,” IEEE Transactions on Medical Imaging, 2023.

[75] L. Zhang, R. Tanno, M. Xu, Y. Huang, K. Bronik, C. Jin, J. Jacob, Y. Zheng, L. Shao, O. Ciccarelli, F. Barkhof, and D. C. Alexander, “Learning from Multiple Annotators for Medical Image Segmentation,” Pattern Recognition, 2023.

[74] H. Zhou, Y. Huang, Y. Li, Y. Zhou, and Y. Zheng, “Blind Super-Resolution of 3D MRI via Unsupervised Domain Transformation,” IEEE Journal of Biomedical and Health Informatics, 2023.

[73] H. Wang, Q. Xie, Q. Zhao, Y. Liang, Y. Zheng, and D. Meng, “RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining,” IEEE Transactions on Neural Networks and Learning Systems, 2023.

[72] H. Wang, Y. Li, H. Zhang, D. Meng, and Y. Zheng, “InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images,” Medical Image Analysis, 2023.

[71] Q. Xu, J. Han, X. Song, Y. Zhao, L. Wu, B. Chu, W. Guo, Y. Zheng, Q. Zhang, C. Chu, C. Bian, K. Ma, and Y. Qu, “The Effectiveness of Artificial Intelligence-based Automated Grading and Training System in Education of Manual Detection of Diabetic Retinopathy,” Frontiers in Public Health, 2022.

[70] Z. Gao, B. Hong, Y. Li, X. Zhang, J. Wu, C. Wang, X. Zhang, T. Gong, Y. Zheng, D. Meng, and C. Li, “A Semi-Supervised Multi-Task Learning Framework for Cancer Classification with Weak Annotation in Whole-Slide Images,” Medical Image Analysis, Vol. 83, 102652, 2023.

[69] D. Du, J. Chen, Y. Li, K. Ma, G. Wu, Y. Zheng, and L. Wang, “Cross-Domain Gated Learning for Domain Generalization,” International Journal of Computer Vision, 2022.

[68] Y. Li, J. Wu, D. Lu, C. Xu, Y. Zheng, H. Peng, and L. Qu, “mBrainAligner-Web: A Web Server for Cross-Modal Coherent Registration of Whole Mouse Brains,” Bioinformatics, 2022.

[67] X. Song, Q. Xu, H. Li, Q. Fan, Y. Zheng, Q. Zhang, C. Chu, M. Ning, C. Bian, K. Ma, and Y. Qu, “Automatic Quantification of Retinal Photoreceptor Integrity to Predict Persistent Disease Activity in Neovascular Age-related Macular Degeneration using Deep Learning,” Frontiers in Neuroscience, 2022.

[66] S. Wang, W. Wang, X. Li, Y. Liu, J. Wei, J. Zheng, Y. Wang, B. Ye, R. Zhao, Y. Huang, S. Peng, Y. Zheng, and Y. Zeng, “Using Machine Learning Algorithms for Predicting Cognitive Impairment and Identifying Modifiable Factors among Chinese Elderly People,” Frontiers in Aging Neuroscience, 2022.

[65] Q. Xie, N. He, Y. Li, M. Ning, K. Ma, G. Wang, Y. Lian, and Y. Zheng, “Unsupervised Domain Adaptation for Medical Image Segmentation by Disentanglement Learning and Self-Training,” IEEE Transactions on Medical Imaging, 2022.

[64] S. Lin, C. Liu, Z.-Y. Hu, P. Zhou, S. Wang, R. Zhao, Y. Zheng, L. Lin, E. Xing, and X. Liang, “Prototypical Graph Contrastive Learning,” IEEE Transactions on Neural Networks and Learning Systems, 2022.

[63] Y. Wang, Y. Li, G. Lin, Q. Zhang, J. Zhong, Y. Zhang, K. Ma, Y. Zheng, G. Lu, and Z. Zhang, “Deep learning-based detection and grading models for lower limb fatigue fracture on radiographs,” European Radiology, 2022.

[62] Z. Gao, C. Jia, Y. Li, X. Zhang, B. Hong, J. Wu, T. Gong, C. Wang, Y. Zheng, D. Meng, and C. Li, “Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast,” IEEE Transactions on Medical Imaging, 2022 (Minor Revision).

[61] J. Zeng, W. Kang, S. Chen, Y. Lin, W. Deng, Y. Wang, G. Chen, K. Ma, F. Zhao, Y. Zheng, M. Liang, L. Zeng, W. Ye, P. Li, Y. Chen, G. Chen, J. Gao, M. Wu, Y. Su, Y. Zheng, and Y. Cai, “A Deep Learning Approach to Predict Conductive Hearing Loss in Otitis Media with Effusion using Otoscopic Images,” JAMA Otolaryngology-Head & Neck Surgery, 2022.

[60] Z. Xu, D. Lu, J. Luo, Y. Wang, J. Yan, K. Ma, Y. Zheng, and R.K. Tong, “Anti-interference from Noisy Labels: Mean-Teacher-assisted Confident Learning for Medical Image Segmentation,” IEEE Transactions on Medical Imaging, 2022 [Download].

[59] M. Antonelli, A. Reinke, S. Bakas, K. Farahni, A. Kopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, B. van Ginneken, M. Bilello, P. Bilic, R. Do, M. Gollub, S. Heckers, H. Huisman, W. R. Jarnagin, M. McHugo, S. Napel, J. G. Pernicka, K. Rhode, C. Tobon-Gomez, E. Vorontsov, J. Meakin, S. Ourselin, M. Wiesenfahrt, P. Arbeláez, B. Bae, S. Chen, L. Daza, J. Feng, F. Jia, F. Isensee, Y. Ji, I. Kim, K. Maier-Hein, D. Merhof, A. Pai, B. Park, M. Perslev, R. Rezaiifar, O. Rippel, I. Sarasua, W. Shen, J. Son, C. Wachinger, Y. Wang, L. Wang, Y. Xia, D. Xu, Z. Xu, Y. Zheng, B. He, A. Simpson, L. Maier-Hein, M. J. Cardoso, and P. Christ, “The Medical Segmentation Decathlon,” Nature Communications, 2022 [Download].

[58] Z. Xu, Y. Wang, D. Lu, L. Yu, J. Yan, J. Luo, K. Ma, Y. Zheng, and R.K. Tong, “All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation,” IEEE Journal of Biomedical and Health Informatics, 2022 [Download].

[57] W. Chen, S. Yu, K. Ma, C. Bian, W. Ji, C. Chu, L. Shen, and Y. Zheng, “TW-GAN: Topology and Width Aware GAN for Retinal Artery/Vein Classification,” Medical Image Analysis, Vol. 77, 102340, 2022.

[56] X. Shi, S. Zhao, Y. Wang, X. Chen, Z. Zhang, Y. Zheng, and W. Che, “Understanding Patient Query with Weak Supervision from Doctor Response,” IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 6, pp. 2770-2777, 2022 [Download].

[55] C. Bian, C. Yuan, K. Ma, S. Yu, D. Wei, and Y. Zheng, “Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation,” IEEE Transactions on Medical Imaging, Vol. 41, No. 5, pp. 1043-1056, 2022 [Download].

[54] H. Wang, Y. Li, N. He, K. Ma, D. Meng, and Y. Zheng, “DICDNet: Deep Interpretable Convolutional Dictionary Network for Metal Artifact Reduction in CT Images,” IEEE Transactions on Medical Imaging, Vol. 41, No. 4, pp. 869-880, 2022 [Download].

[53] J. Sun, Y. Zheng, W. Liang, Z. Yang, Z. Zeng, T. Li, J. Luo, M.T.A. Ng, J. He, and N. Zhong, “Quantifying the Effect of Public Activity Intervention Policies on COVID-19 Pandemic Containment using Epidemiological Data from 145 Countries,” Value in Health, Vol. 25, No. 5, pp. 699-708, 2022.

[52] Y. Li, J. Chen, D. Wei, Y. Zhu, J. Wu, T. Qian, K. Ma, and Y. Zheng, “Mix-and-Interpolate: A Training Strategy to Deal with Source-biased Medical Data,” IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 1, pp. 172-182, 2022 [Download].

[51] J. Chen, Z. Zhang, X. Xie, Y. Li, T. Xu, K. Ma, and Y. Zheng, “Beyond Mutual Information: Generative Adversarial Network for Domain Adaptation using Information Bottleneck Constraint,” IEEE Transactions on Medical Imaging, Vol. 4, No. 3, pp. 595-607, 2022 [Download].

[50] Y. Shi, J. Zhang, T. Ling, J. Lu, Y. Zheng, Q. Yu, L. Qi, and Y. Gao, “Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation,” IEEE Transactions on Medical Imaging, Vol. 4, No. 3, pp. 608-620, 2022 [Download].

[49] X. Han, L. Qi, Q. Yu, Z. Zhou, Y. Zheng, Y. Shi, and Y. Gao, “Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation,” IEEE Transactions on Medical Imaging, Vol. 41, No. 1, pp. 121-132, 2022 [Download].

[48] L. Qu, Y. Li, P. Xie, L. Liu, Y. Wang, J. Wu, Y. Liu, T. Wang, L. Li, K. Guo, W. Wan, L. Ouyang, F. Xiong, Y. Zheng, F. Xu, H. Gong, H. Dong, Q. Luo, G. Bi, M. Hawrylycz, H. Zeng, and H. Peng, “Cross-Modality Coherent Registration of Whole Mouse Brains,” Nature Methods, Vol. 19, No. 1, pp. 111–118, 2022.

[47] H. Peng, P. Xie, Y. Wang, L. Liu, Y. Wang, L. Qu, H. Gong, X. Kuang, S. Jiang, A. Li, M. Chen, T. L. Daigle, L. Ding, Y. Duan, A. Feiner, P. He, C. Hill, K. E. Hirokawa, L. Huang, S. Kebede, H.-C. Kuo, R. Larsen, P. Lesnar, Q. Li, X. Li, Y. Li, Y. Li, D. Lu, S. Mok, L. Ng, T. N. Nguyen, Q. Ouyang, Z. Ruan, E. Shen, Y. Song, S. M. Sunkin, M. B. Veldman, W. Wakeman, W. Wan, P. Wang, Q. Wang, T. Wang, Y. Wang, F. Xiong, W. Xu, L. Yin, Y. Yu, J. Yuan, Z. Yun, S. Zeng, S. Zhang, S. Zhao, X. Zhao, Z. Zhou, Z. J. Huang, L. Esposito, Z. Yao, B. Tasic, M. J. Hawrylycz, S. A. Sorensen, X. W. Yang, Y. Zheng, Z. Gu, W. Xie, C. Koch, Q. Luo, J. A. Harris, and H. Zeng, “Complete Whole-Brain Single Neuron Reconstruction Reveals Morphological Diversity in Molecularly Defined Striatal, Thalamic and Cortical Neuron Types,” Nature, Vol. 598, pp.174–181, 2021 [Download].

[46] Y. Li, D. Wei, X. Liu, X. Fan, K. Wang, S. Li, Z. Zhang, K. Ma, T. Qian, T. Jiang, Y. Zheng, and Y. Wang, “Molecular Subtyping of Diffuse Gliomas using Magnetic Resonance Imaging: Comparison and Correlation between Radiomics and Deep Learning,” European Radiology, 2021 [Download].

[45] L. Liu, Z. Zhang, S. Li, K. Ma, and Y. Zheng, “S-CUDA: Self-Cleansing Unsupervised Domain Adaptation for Medical Image Segmentation,” Medical Image Analysis, Vol. 74, 102214, 2021 [Download].

[44] H. Zhao, Y. Li, N. He, K. Ma, L. Fang, H. Li, and Y. Zheng, “Anomaly Detection for Medical Images using Self-supervised and Translation-consistent Features,” IEEE Transactions on Medical Imaging, Vol. 40, No. 12, pp. 3641-3651, 2021 [Download].

[43] X. Hong, Q. Zheng, L. Liu, P. Chen, K. Ma, Z. Gao, and Y. Zheng, “Dynamic Joint Adversarial Network for Motor Imagery Classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 29, pp. 556-565, 2021 [Download].

[42] Y. Li, Z.-H. Liu, P. Xue, J. Chen, K. Ma, T. Qian, Y. Zheng, and Y.-L. Qiao, “GRAND: A Large-scale Dataset and Benchmark for Cervical Intraepithelial Neoplasia Grading with Fine-grained Lesion Description,”Medical Image Analysis, Vol. 70, 2021 [Download].

[41] H. Cui, D. Wei, K. Ma, S. Gu, and Y. Zheng, “A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data,”IEEE Transactions on Medical Imaging, Vol. 40, No. 10, pp. 2656-2671, 2021 [Download].

[40] H. Zhao, Q. Zheng, K. Ma, H. Li, and Y. Zheng, “Deep Representation-based Domain Adaptation for Non-stationary EEG Classification,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 2, pp. 535-545, 2021 [Download].

[39] Z. Xiong, Q. Xia, Z. Hu, N. Huang, C. Bian, Y. Zheng, S. Vesal, N. Ravikumar, A. Maier, X. Yang, P.-A. Heng, D. Ni, C. Li, Q. Tong, W. Si, E. Puybareau, Y. Khoudli, T. Géraud, C. Chen, W. Bai, D. Rueckert, L. Xu, X. Zhuang, X. Luo, S. Jia, M. Sermesant, Y. Liu, K. Wang, D. Borra, A. Masci, C. Corsi, C. de Vente, M. Veta, R. Karim, C. J. Preetha, S. Engelhardt, M. Qiao, Y. Wang, Q. Tao, M. Nuñez-Garcia, O. Camara, N. Savioli, P. Lamata, and J. Zhao, “A Global Benchmark of Algorithms for Segmenting the Left Atrium from Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging,” Medical Image Analysis, Vol. 67, 101832, pp. 1-11, 2021 [Download].

[38] R. Wang, S. Cao, K. Ma, Y. Zheng, and D. Meng, “Pairwise Learning for Medical Image Segmentation,” Medical Image Analysis, Vol. 67, 101876, pp. 1-11, 2021 [Download].

[37] P. Xue, C. Tang, Q. Li, Y. Li, Y. Shen, Y. Zhao, J. Chen, J. Wu, L. Li, W. Wang, Y. Li, X. Cui, S. Zhang, W. Zhang, X. Zhang, K. Ma, Y. Zheng, T. Qian, M. T. A. Ng, Z. Liu, and Y.-L. Qiao, “Development and Validation of an Artificial Intelligence System for Grading Colposcopic Impressions and Guiding Biopsies,” BMC Medicine, Vol. 10, pp. 1-10, 2020 [Download].

[36] Q. Yu, Y. Gao, Y. Zheng, J. Zhu, Y. Dai, and Y. Shi, “Crossover-Net: Leveraging Vertical-horizontal Crossover Relation for Robust Medical Image Segmentation,” Pattern Recognition, Vol. 113, 107756, 2020 [Download].

[35] J. Sun, X. Chen, Z. Zhang, S. Lai, S. Wang, W. Huan, R. Zhao, M. T. A. Ng, and Y. Zheng, “Forecasting the Long-term Trend of COVID-19 Epidemic using a Dynamic Model,” Scientific Reports, Vol. 10, 21122, pp. 1-9, 2020 [Download].

[34] Y.-Q. Jiang, S.-E. Cao, S. Cao, J.-N. Chen, G.-Y. Wang, W.-Q. Shi, Y.-N. Deng, N. Cheng, K. Ma, K.-N. Zeng, X.-J. Yan, H.-Z. Yang, W.-J. Huan, W.-M. Tang, Y. Zheng, C.-K. Shao, J. Wang, Y. Yang, and G.-H. Chen, “Preoperative Identification of Microvascular Invasion in Hepatocellular Carcinoma by XGBoost and Deep Learning,” Journal of Cancer Research and Clinical Oncology, pp. 1-13, Aug., 2020 [Download].

[33] S. Wang, S. Cao, Z. Chai, D. Wei, K. Ma, L. Wang, and Y. Zheng, “Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network,” IEEE Transactions on Medical Imaging, Vol. 39, No. 12, pp. 4174-4185, 2020 [Download].

[32] Y. Li, D. Wei, J. Chen, S. Cao, H. Zhou, Y. Zhu, J. Wu, T. Qian, K. Ma, and Y. Zheng, “Efficient and Effective Training of COVID-19 Classification Networks with Self-supervised Dual-track Learning to Rank,” IEEE Journal of Biomedical and Health Informatics, Vol. 24, No, 10, pp. 2787-2797, 2020 [Download].

[31] J. Zhu, Y. Li, Y. Hu, K. Ma, S. K. Zhou, and Y. Zheng, “Rubik’s Cube+: A Self-supervised Feature Learning Framework for 3D Medical Image Analysis,” Medical Image Analysis, 2020 [Download].

[30] Y. Li, J. Chen, P. Xue, C. Tang, J. Chang, C. Chu, K. Ma, Q. Li, Y. Zheng, and Y. Qiao, “Computer-aided Cervical Cancer Diagnosis using Time-lapsed Colposcopic Images,” IEEE Transactions on Medical Imaging, 2020 [Download].

[29] C. Yuan, C. Bian, J. Wang, M. Li, X. Yang, S. Yu, K. Ma, J. Yuan, and Y. Zheng, “Uncertainty-aware Domain Alignment for Anatomical Structure Segmentation,” Medical Image Analysis, 2020 [Download].

[28] S. A. Taghanaki, Y. Zheng, S. K. Zhou, B. Georgescu, P. Sharma, D. Xu, D. Comaniciu, and G. Hamarne,“Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation,” Computerized Medical Imaging and Graphics, Vol. 75, pp. 24-33, 2019 [Download].

[27] J. Cai, Z. Zhang, L. Cui, Y. Zheng, and L. Yang,“Towards Cross-Modal Organ Translation and Segmentation: A Cycle- and Shape-Consistent Generative Adversarial Network,”Medical Image Analysis, Vol. 52, pp. 174-184, 2019 [Download].

[26] F.C. Ghesu, B. Georgescu, Y. Zheng, S. Grbic, A. Maier, J. Hornegger, D. Comaniciu, “Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, No. 1, pp. 176-189, 2019  [Download].

[25] F. C. Ghesu, E. Krubasik, B. Georgescu, V. Singh, Y. Zheng, J. Hornegger, and D. Comaniciu, “Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing,” IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp. 1217-1228, 2016 [Download].

[24] X. Cheng, L. Zhang, and Y. Zheng, “Deep Similarity Learning for Multimodal Medical Images,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2016 [Download].

[23] J. M. Wolterink, T. Leiner, J.-L. Coatrieux, B. M. Kelm, S. Kondo, R. A. Salgado, R. Shahzad, H. Shu, M. Snoeren, R. A.P. Takx, L. van Vliet, B. D. de Vos, T. van Walsum, T. P. Willems, G. Yang, Y. Zheng, M. A. Viergever, and I. Isgum, “An Evaluation of Automatic Coronary Artery Calcium Scoring with Cardiac CT: the orCaScore Challenge,” Medical Physics, Vol. 43, No. 5, pp. 2361-2373, 2016 [Download].

[22] Q. Wang, L. Lu, D. Wu, N. El-Zehiry, Y. Zheng, D. Shen, and S. K. Zhou, “Automatic Segmentation of Spinal Canals in CT Images via Iterative Topology Refinement,” IEEE Transactions on Medical Imaging, Vol. 34, No. 8, pp. 1694-1704, 2015 [Download].

[21] K. Ralovich, L. Itu, D. Vitanovski, P. Sharma, R. Ionasec, V. Mihalef, W. Krawtschuk, Y. Zheng, A. Everett, G. Pongiglione, B. Leonardi, R. Ringel, N. Navab, T. Heimann, and D. Comaniciu, “Non-invasive Hemodynamic Assessment, Treatment Outcome Prediction and Follow-up of Aortic Coarctation from MR Imaging,” Medical Physics, Vol. 42, pp. 2143-2156, 2015 [Download].

[20] C. Tobon-Gomez, A. J. Geers, J. Peters, J.Weese, K. Pinto, M. Ammar, A. Daoudi, J. Margeta, Z. Sandoval, B. Stender, Y. Zheng, M. Zuluaga, J. Betancur, N.Ayache, J. L. Dillenseger, R. Karim, M. Kelm, S. Mahmoudi, S. Ourselin, R. Razavi, T. Schaeffter, A. Schlaefer, and K. S. Rhode, “Benchmark for Left Atrial Segmentation Algorithms from 3D CT and MRI Datasets,” IEEE Transactions on Medical Imaging, Vol. 34, No. 7, pp. 1460- 1473, 2015 [Download].

[19] K. Muller, A. K. Maier, Y. Zheng, Y. Wang, G. Lauritsch, C. Schwemmer, C. Rohkohl, R. Fahrig, and J. Hornegger, “Interventional Heart Wall Motion Analysis with Cardiac C-arm CT Systems,” Physics in Medicine & Biology, Vol. 59, No. 9, pp. 2265-2284, 2014 [Download].

[18] Y. Zheng, D. Yang, M. John, and D. Comaniciu, “Multi-Part Modeling and Segmentation of Left Atrium in C-Arm CT for Image-Guided Atrial Fibrillation Ablation,” IEEE Transactions on Medical Imaging, Vol. 33, No. 2, pp. 318-331, 2014 [Download].

[17] B. M. Kelm, M. Wels, S. K. Zhoub, S. Seifert, M. Suehling, Y. Zheng, and D. Comaniciu, “Spine Detection in CT and MR Using Iterated Marginal Space Learning,” Medical Image Analysis, Vol. 17, No. 8, pp. 1283-1292, 2013 [Download].

[16] M. Chen, K. Cao, Y. Zheng, and R. A. C. Siochi, “Motion-Compensated Mega-Voltage Cone Beam CT Using the Deformation Derived Directly from 2D Projection Images,” IEEE Transactions on Medical Imaging, Vol. 32, pp. 1365-1375, 2013 [Download].

[15] R. Liao, S. Miao, and Y. Zheng, “Automatic and Efficient Contrast-based 2-D/3-D Fusion for Trans-catheter Aortic Valve Implantation (TAVI),” Computerized Medical Imaging and Graphics, Vol. 37, No. 2, pp. 150-161, 2013 [Download].

[14] K. Muller, C. Schwemmer, J. Hornegger, Y. Zheng, Y. Wang, G. Lauritsch, C. Rohkohl, A. K. Maier, C. Schultz, and R. Fahrig, “Evaluation of Interpolation Methods for Surface-based Motion Compensated Tomographic Reconstruction for Cardiac Angiographic C-arm Data,” Medical Physics, Vol. 40, No. 3, pp. 1-12, 2013 [Download].

[13] Y. Zheng, M. John, R. Liao, A. Nottling, J. Boese, J. Kempfert, T. Walther, G. Brockmann, and D. Comaniciu, “Automatic Aorta Segmentation and Valve Landmark Detection in C-Arm CT for Transcatheter Aortic Valve Implantation,” IEEE Transactions on Medical Imaging, Vol. 31, No. 12, pp. 2307-2321,2012 [Download].

[12] L. Yang, B. Georgescu, Y. Zheng, Y. Wang, P. Meer, and D. Comaniciu, “Predication Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking,” IEEE Transactions on Medical Imaging, Vol. 30, No. 11, pp. 1921-1932, 2011 [Download].

[11] M. Wels, Y. Zheng, M. Huber, J. Hornegger, and D. Comaniciu, "A Discriminative Model-Constrained EM Approach to 3-D MRI Brain Tissue Classification and Intensity Non-Uniformity Correction," Physics in Medicine & Biology, Vol. 56, No. 11, pp. 3269-3300, 2011 [Download].

[10] G. Zhu, Y. Zheng, D. Doermann, and S. Jaeger, "Signature Detection and Matching for Document Image Retrieval,"IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 11, pp. 2015-2031, 2009 [Download].

[9] Y. Zheng, A. Barbu, B. Georgescu, Michael Scheuering, and D. Comaniciu, "Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes Using Marginal Space Learning and Steerable Featurs," IEEE Transactions on Medical Imaging, Vol. 27, No. 11, pp. 1668-1681, 2008 [Download].

[8] Y. Li, Y. Zheng, D. Doermann, and S. Jaeger, "Script-Independent Text Line Segmentation in Freestyle Handwritten Documents,"IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 8, pp. 1313-1329, 2008 [Download].

[7] M. Chen, Y. Zheng, and M. Wu, "Classification-Based Spatial Error Concealment for Visual Communications,"  EURASIP Journal on Applied Signal Processing, Vol. 2006, pp. 1-17, 2006 [Download]

[6] Y. Zheng and D. Doermann, "Robust Point Matching for Nonrigid Shapes by Preserving Local Neighborhood Structures," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, pp. 643-649, 2006 [Download].

[5] Y. Zheng, H. Li and Doermann, "A Parallel-Line Detection Algorithm Based on HMM Decoding,"  IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, pp.777-792, 2005 [Download].              

[4] Y. Zheng, H. Li, and D. Doermann, "Machine Printed Text and Handwriting Identification in Noisy Document Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 3, pp. 337-353, 2004 [Download].  

[3] C. Liu, S. Pan, Y. Zheng,  and X. Ding, "Form Line Detection and Removal Algorithm for Form Document Recognition," Journal of Electronics and Information (Chinese),  Vol. 24, No. 9, pp. 1190-1196, 2002.

[2] Y. Zheng, C. Liu, X. Ding, and S. Pan, "Form Line Detection with Directional Single-Connected Chain," Journal of Software (Chinese),  Vol. 13, No.4, pp. 790-796, 2002.

[1] Y. Zheng, C. Liu, and X. Ding, "Form Frame Line Removal with Line Width Threshold Approach," Pattern Recognition and Artificial Intelligence (Chinese), Vol. 14, No. 2, pp. 210-214, 2001.

Conference Publications

[223] Z. Lin, Z. Zhang, X. Wu, and Y. Zheng, “Improving Biomedical Entity Linking with Retrieval-Enhanced Learning,” Proc. IEEE Int’ l Conf. Acoustics, Speech and Signal Processing, 2024.

[222] Q. Bi, H. Zheng, X. Sun, J. Yi, W. Zhang, Y. Huang, Y. Li, and Y. Zheng, “Self-supervised Cross-level Consistency Learning for Fundus Image Classification,” Proc. IEEE Int’ l Conf. Acoustics, Speech and Signal Processing, 2024.

[221] X. Gong, S. Li, Y. Bao, B. Yao, Y. Huang, Z. Wu, B. Zhang, Y. Zheng, and D. Doermann, “Federated Learning via Input-Output Collaborative Distillation,” Proc. AAAI Conf. Artificial Intelligence, 2024.

[220] R. Zhang, D. Cheng, J. Yang, Y. Ouyang, X. Wu, Y. Zheng, and C. Jiang, “Contrastive Learning for Pre-trained Online Model in Insurance Fraud Detection,” Proc. AAAI Conf. Artificial Intelligence, 2024.

[219] Y. Cheng, W. Liu, J. Wang, C. T. Leong, Y. Ouyang, W. Li, X. Wu, and Y. Zheng, “Cooper: Coordinating Specialized Agents towards a Complex Dialogue Goal,” Proc. AAAI Conf. Artificial Intelligence, 2024.

[218] Q. Bi, J. Yi, H. Zheng, W. Ji, Y. Huang, Y. Li, and Y. Zheng, “Learning Generalized Medical Image Segmentation from Decoupled Feature Queries,” Proc. AAAI Conf. Artificial Intelligence, 2024.

[217] Q. Dai, D. Wei, H. Liu, J. Sun, L. Wang, and Y. Zheng, “Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation,” Proc. AAAI Conf. Artificial Intelligence, 2024.

[216] H. Huang, Y. Huang, S. Xie, L. Lin, R. Tong, Y.-W. Chen, Y. Li, and Y. Zheng, “Combinatorial CNN-Transformer Learning with Manifold Constraints for Semi-Supervised Medical Image Segmentation,” Proc. AAAI Conf. Artificial Intelligence, 2024.

[215] H. Zhang, J. Li, L. Chen, R. Cao, Y. Zhang, Y. Huang, Y. Zheng, and K. Yu, “CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset,” Findings of Annual Conf. Association for Computational Linguistics, 2023.

[214] Z. Ye, W. Jiang, B. Liu, R. Zhao, J. Zheng, M. Li, Z. Li, Y. Yang, and Y. Zheng, “ICA-Proto: Iterative Cross Alignment Prototypical Network for Incremental Few-Shot Relation Classification,” Findings of the Conference of European Chapter of the Association for Computational Linguistics, 2023.

[213] L. Zhou, D. Wei, D. Lu, W. Xue, L. Wang, and Y. Zheng, “RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training,” Proc. IEEE Int’ l Symp. Biomedical Imaging, 2023.

[212] L. Yue, Y. Zhang, Q. Yao, Y. Li, X. Wu, Z. Zhang, Z. Lin, and Y. Zheng, “Relation-aware Ensemble Learning for Knowledge Graph Embedding,” Proc. Conf. Empirical Methods in Natural Language Processing, 2023.

[211] J. Xie, K. Ye, Y. Li, Y. Li, K. Q. Lin, Y. Zheng, L. Shen, and M. Z. Shou, “VisorGPT: Learning Visual Prior via Generative Pre-Training,” Advances in Neural Information Processing Systems, 2023.

[210] W. Zhang, H. Liu, B. Li, J. Xie, Y. Huang, Y. Li, Y. Zheng, and B. Ghanem, “Dynamically Masked Discriminator for Generative Adversarial Networks,” Advances in Neural Information Processing Systems, 2023.

[209] H. Huang, Y. Huang, S. Xie, L. Lin, R. Tong, Y. Chen, Y. Li, and Y. Zheng, “Semi-Supervised Convolutional Vision Transformer with Bi-Level Uncertainty Estimation for Medical Image Segmentation,” Proc. ACM Conf. Multimedia, 2023.

[208] J. Xie, Y. Li, Y. Huang, H. Liu, W. Zhang, Y. Zheng, and M. Z. Shou, “BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion,” Proc. Int’l Conf. Computer Vision, 2023.

[207] P. Tu, X. Xie, A. Guo, Y. Li, Y. Huang, and Y. Zheng, “FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs,” Proc. Int’l Conf. Computer Vision, 2023.

[206] H. W, M. Zhou, D. Wei, Y. Li, and Y. Zheng, “MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2023.

[205] Z. Xu, J. Yan, D. Lu, Y. Wang, J. Luo, Y. Zheng, and K. Tong, “Towards Expert-Amateur Collaboration: Prototypical Label Isolation Learning for Left Atrium Segmentation with Mixed-Quality Labels,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2023.

[204] J. Sun, D. Wei, Z. Xu, D. Lu, H. Liu, L. Wang, and Y. Zheng, “You’ve Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2023.

[203] Y. Zhang, D. Lu, D. Wei, M. Ning, L. Wang, and Y. Zheng, “A Model-Agnostic Framework for Universal Anomaly Detection of Multi-Organ and Multi-Modal Images,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2023.

[202] Z. Xu, D. Lu, J. Yan, J. Sun, J. Luo, D. Wei, S. Frisken, Q. Li, Y. Zheng, and K. Tong, “Category-level Regularized Unlabeled-to-labeled Learning for Semi-supervised Prostate Segmentation with Multi-site Unlabeled Data,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2023.

[201] H. Wang, W. C. Kwan, K.-F. Wong, and Y. Zheng, “CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation,” Proc. Annual Conf. Association for Computational Linguistics, 2023.

[200] J. Ma, F. Li, R. Zhang, Z. Xu, D. Cheng, Y. Ouyang, R. Zhao, J. Zheng, Y. Zheng, and C. Jiang, “Fighting against Organized Fraudsters Using Risk Diffusion-based Parallel Graph Neural Network,” Proc. Int’l Joint Conf. Artificial Intelligence, 2023.

[199] R. Zhou, X. Wu, Z. Qiu, Y. Zheng, and X. Chen, “Distributionally Robust Sequential Recommendation,” Proc. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, 2023.

[198] H. Liu, W. Zhang, B. Li, H. Wu, N. He, Y. Huang, Y. Li, B. Ghanem, and Y. Zheng, “AdaptiveMix: Robust Feature Representation via Shrinking Feature Space,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2023.

[197] H. Huang, S. Xie, L. Lin, T. Ruofeng, Y.-W. Chen, Y. Li, H. Wang, Y. Huang, and Y. Zheng, “SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2023.

[196] M. Zhou, H. Wang, Q. Zhao, Y. Li, Y. Huang, D. Meng, and Y. Zheng, “Interactive Segmentation as Gaussian Process Classification,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2023.

[195] J. Wu, X. Wu, Y. Hua, S. Lin, Y. Zheng, and J. Yang, “Exploring Social Media for Early Detection of Depression in COVID-19 Patients,” Proc. the Web Conference, 2023.

[194] S. Wang, M. Ju, Y. Zhang, Y. Zheng, M. Wang, and G. Qi, “Cross-modal Contrastive Learning for Event Extraction,” Proc. Int’l Conf. Database Systems for Advanced Applications, 2023.

[193] S. Xiang, M. Zhu, D. Cheng, E. Li, R. Zhao, Y. Ouyang, L. Chen, and Y. Zheng, “Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation,” Proc. AAAI Conf. Artificial Intelligence, 2023.

[192] S. Wu, R. Zhao, Y. Zheng, J. Pei, and B. Liu, “Identify Event Causality with Knowledge and Analogy,” Proc. AAAI Conf. Artificial Intelligence, 2023.

[191] H. Liu, B. Li, H. Wu, H. Liang, Y. Huang, Y. Li, B. Ghanem, and Y. Zheng, “Combating Mode Collapse via Offline Manifold Entropy Estimation,” Proc. AAAI Conf. Artificial Intelligence, 2023.

[190] H. Liu, D. Wei, D. Lu, J. Sun, L. Wang, and Y. Zheng, “M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities,” Proc. AAAI Conf. Artificial Intelligence, 2023.

[189] H. Huang, S. Xie, L. Lin, R. Tong, Y.-W. Chen, H. Wang, Y. Li, Y. Huang, and Y. Zheng, “ClassFormer: Exploring Class-aware Dependency with Transformer for Medical Image Segmentation,” Proc. AAAI Conf. Artificial Intelligence, 2023.

[188] L. Gao, X. Zhang, X. Wu, S. Ge, and Y. Zheng, “Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation,” Findings of the Conference on Empirical Methods in Natural Language, 2023.

[187] Z. Chen, Z. Wu, X. Wu, L. Zhang, J. Zhao, Y. Yan, and Y. Zheng, “Contractible Regularization for Federated Learning on Non-IID Data,” Proc. IEEE Int’l Conf. Data Mining, 2022.

[186] X. Zhang, B. Qian, Y. Li, Z. Gao, C. Guan, R. Wang, C. Li, and Y. Zheng, “Learning Representations from Local to Global for Fine-grained Patient Similarity Measuring in Intensive Care Unit,” Proc. IEEE Int’l Conf. Data Mining, 2022.

[185] Z. Wang, R. Wen, X. Chen, S.-L. Huang, N. Zhang, and Y. Zheng, “Finding Influential Instances for Distantly Supervised Relation Extraction,” Proc. Int’l Conf. Computational Linguistics, 2022.

[184] H. Wang, C. Liu, N. Xi, S. Zhao, M. Ju, S. Zhang, Z. Zhang, Y. Zheng, B. Qin, and T. Liu, “Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words,” Proc. Int’l Conf. Computational Linguistics, 2022.

[183] X. Wu, S. Yang, Z. Qiu, S. Ge, Y. Yan, X. Wu, Y. Zheng, S. K. Zhou, and L. Xiao, “DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis,” Proc. Int’l Conf. Computational Linguistics, 2022.

[182] Z. Lin, Z. Zhang, M. Wang, Y. Shi, X. Wu, and Y. Zheng, “Multi-modal Contrastive Representation Learning for Entity Alignment,” Proc. Int’l Conf. Computational Linguistics, 2022.

[181] W. Jiang, Z. Ye, Z. Ou, R. Zhao, J. Zheng, Y. Liu, S. Li, B. Liu, Y. Yang, and Y. Zheng, “MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction,” Proc. ACM Int’l Conf. Information and Knowledge Management, 2022.

[180] J. Wu, B. Qian, Y. Li, Z. Gao, M. Ju, Y. Yang, Y. Zheng, T. Gong, C. Li, and X. Zhang, “Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation,” Proc. ACM Int’l Conf. Information and Knowledge Management, 2022.

[179] Y. Huang, F. Zheng, X. Sun, Y. Li, L. Shao, and Y. Zheng, “Generalized Brain Image Synthesis with Transferable Convolutional Sparse Coding Networks,” Proc. European Conf. Computer Vision, 2022.

[178] X. Shi, D. Wei, D. Lu, J. Chen, M. Ning, K. Ma, Y. Zhang, and Y. Zheng, “Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation,” Proc. European Conf. Computer Vision, 2022.

[177] J. Wang, G. Xie, Y. Huang, Y. Zheng, Y. Jin, and F. Zheng, “FedMed-ATL: Misaligned Unpaired Cross-Modality Neuroimage Synthesis via Affine Transform Loss,” Proc. ACM Int’ l Conf. Multimedia, 2022.

[176] S. Zhang, J. Sun, Y. Huang, X. Ding, and Y. Zheng, “Medical Symptom Detection in Intelligent Pre-Consultation using Bi-directional Hard-Negative Noise Contrastive Estimation,” Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022.

[175] J. Ren, T. Cao, Y. Yang, Y. Zhang, X. Chen, T. Feng, B. Chang, Z. Sui, R. Zhao, Y. Zheng, and B. Liu, “CLINER: Clinical Interrogation Named Entity Recognition,” Proc. Int’l Conf. Knowledge Science, Engineering and Management, 2022.

[174] Y. Zhang, N. He, J. Yang, Y. Li, D. Wei, Y. Huang, Y. Zhang, Z. He, and Y. Zheng, “mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[173] H. Ji, H. Liu, J. Xie, Y. Li, N. He, Y. Huang, D. Wei, X. Chen, L. Shen, and Y. Zheng, “Point Beyond Class: A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[172] J. Sun, D. Wei, L. Wang, and Y. Zheng, “Lesion Guided Explainable Few Weak-shot Medical Report Generation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[171] W. Zhang, X. Sun, H. Liu, Y. Li, N. He, F. Liu, and Y. Zheng, “A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[170] W. Yuan, D. Lu, D. Wei, M. Ning, and Y. Zheng, “Multiscale Unsupervised Retinal Edema Area Segmentation in OCT Images,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[169] J. Chen, D. Lu, D. Wei, M. Ning, X. Shi, Z. Xu, Y. Zheng, and Y. Zhang, “Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[168] H. Wang, Q. Xie, Y. Li, Y. Huang, D. Meng, and Y. Zheng, “Orientation-Shared Convolution Representation for CT Metal Artifact Learning,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[167] Z. Xu, D. Lu, Y. Wang, J. Luo, D. Wei, Y. Zheng, and K.-Y. Tong, “Denoising for Relaxing: Unsupervised Domain Adaptive Fundus Image Segmentation without Source Data,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[166] M. Ning, D. Lu, D. Wei, L. Wang, J. Chen, and Y. Zheng, “An Inclusive Task-Aware Framework for Radiology Report Generation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022.

[165] Z. Xu, J. Luo, D. Lu, J. Yan, S. Frisken, J. Jayender, W. Wells, X. Li, Y. Zheng, and K.-Y. Tong, “Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2022 [Download].

[164] W. Liu, Y. Cheng, H. Wang, J. Tang, Y. Liu, R. Zhao, W. Li, Y. Zheng, and X. Liang, “‘My nose is running.’ ‘Are you also coughing?’: Building A Medical Diagnosis Agent with Interpretable Inquiry Logics,” Proc. Int’l Joint Conf. Artificial Intelligence, 2022 [Download].

[163] H. Wang, Y. Li, D. Meng, and Y. Zheng, “Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction,” Proc. Int’l Joint Conf. Artificial Intelligence, 2022 [Download].

[162] J. Ding, T. Xiang, Z. Ou, W. Zuo, R. Zhao, C. Lin, Y. Zheng, and B. Liu, “Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation,” Proc. IEEE Int’ l Conf. Data Engineering, 2022 [Download].

[161] X. Ma, M. Wang, X. Liu, Y. Yang, Y. Zheng, and S. Wang, “InDISP: An Interpretable Model for Dynamic Illness Severity Prediction,” Proc. Int’l Conf. Database Systems for Advanced Applications, 2022 [Download].

[160] Z. Wang, S.-L. Huang, E. E. Kuruoglu, J. Sun, X. Chen, and Y. Zheng, “PAC-Bayes Information Bottleneck,” Proc. Int’l Conf. Learning Representations, 2022 [Download].

[159] Y. Wang, R. Zhao, Y. Zheng, and B. Liu, “QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations,” Proc. the Web Conference, 2022 [Download].

[158] W. Liu, Y. Cheng, J. Tang, W. Li, Y. Zheng, and X. Liang, “MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation,” Proc. CCF Conf. Natural Language Processing and Chinese Computing, 2022

[157] Z. Ye, Z. Wang, R. Wen, X. Chen, Z. Li, and Y. Zheng, “Knowledge-driven Matching Empowered Gated Graph Attention Network for Inductive Short Text Classification,” Proc. Int’l Joint Conf. Neural Networks, 2022.

[156] J. Li, Y. Zhang, Y. Yang, Z. An, and Y. Zheng, “BNU: A Balance-Normalization-Uncertainty Model for Incremental Event Detection,” Proc. IEEE Int’ l Conf. Acoustics, Speech and Signal Processing, 2022.

[155] Y. Li, X. Zhang, B. Qian, Z. Gao, C. Guan, Y. Zheng, H. Zheng, F. Wu, and C. Li “Towards Interpretability and Personalization: A Predictive Framework for Clinical Time-series Analysis,” Proc. IEEE Int’l Conf. Data Mining, 2021.

[154] Z. Zhang, Y. Li, H. Wei, K. Ma, T. Xu, and Y. Zheng, “Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix,” Proc. British Machine Vision Conference, 2021 [Download].

[153] Z. Ou, Q. Su, J. Yu, R. Zhao, Y. Zheng, and B. Liu, “Refining BERT Embeddings for Document Hashing via Mutual Information Maximization,” Findings of the Conference on Empirical Methods in Natural Language Processing, 2021 [Download].

[152] Z. Qi, Z. Zhang, J. Chen, X. Chen, and Y. Zheng, “PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System,” Proc. Conf. Information and Knowledge Management (demo track), 2021 [Download].

[151] J. Sun, D. Wei, K. Ma, L. Wang, and Y. Zheng, “Boost Supervised Pretraining for Visual Transfer Learning: Implications of Self-Supervised Contrastive Representation Learning,” Proc. AAAI Conf. Artificial Intelligence, 2022 [Download].

[150] Y. Cheng, W. Liu, W. Li, J. Wang, R. Zhao, B. Liu, X. Liang, and Y. Zheng, “Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning,” Proc. Conf. Empirical Methods in Natural Language Processing, 2022.

[149] H. Wang, Y. Li, H. Zhang, J. Chen, K. Ma, D. Meng, and Y. Zheng, “InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[148] J. Sun, D. Wei, K. Ma, L. Wang, and Y. Zheng, “Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification,”Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[147] Z. Xu, D. Lu, Y. Wang, J. Luo, J. Jayender, K. Ma, Y. Zheng, and X. Li, “Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[146] S. Yu, K. Ma, Q. Bi, C. Bian, M. Ning, N. He, Y. Li, H. Liu, and Y. Zheng, “MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classification,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[145] Q. Bi, S. Yu, W. Ji, C. Bian, L. Gong, H. Liu, K. Ma, and Y. Zheng, “Local-global Dual Perception based Deep Multiple Instance Learning for Retinal Disease Classification,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[144] D. Wei, K. Ma, and Y. Zheng, “Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[143] Q. Yao, Z. He, Y. Lin, K. Ma, Y. Zheng, and S. K. Zhou, “A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[142] Y. Li, Y. Wang, G. Lin, Y. Lin, D. Wei, Q. Zhang, K. Ma, G. Lu, Z. Zhang, and Y. Zheng, “Triplet-Branch Network with Prior-Knowledge Embedding for Fatigue Fracture Grading,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[141] Y. Lin, L. Liu, K. Ma, and Y. Zheng, “Seg4Reg+: A Local and Global Consistency Learning between Spine Segmentation and Cobb Angle Regression,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[140] Y. Li, N. He, K. Ma, and Y. Zheng, “Deep Reinforcement Exemplar Learning for Annotation Refinement,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[139] H. Liu, D. Wei, D. Lu, Y. Li, K. Ma, L. Wang, and Y. Zheng, “Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2021 [Download].

[138] H. Zheng, R. Wen, X. Chen, Y. Yang, Y. Zhang, Z. Zhang, N. Zhang, B. Qin, X. Ming, and Y. Zheng, “PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction,” Proc. Annual Conf. Association for Computational Linguistics, 2021 [Download].

[137] Z. Ou, Q. Su, J. Yu, J. Wang, R. Zhao, C. Chen, Y. Zheng, and B. Liu, “Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval,” Proc. Annual Conf. Association for Computational Linguistics, 2021 [Download].

[136] Y. Cheng, S. Li, R. Zhao, S. Li, C. Lin, Y. Zheng, and B. Liu, “Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting,” Proc. Annual Conf. Association for Computational Linguistics, 2021 [Download].

[135] Z. Qi, Z. Zhang, J. Chen, X. Chen, Y. Xiang, N. Zhang, and Y. Zheng, “Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding,” Proc. Int’l Joint Conf. Artificial Intelligence, 2021 [Download].

[134] Z. Hou, B. Liu, R. Zhao, Z. Ou, Y. Liu, X. Chen, and Y. Zheng, “Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management,” Proc. Annual Conf. the North American Chapter of the Association for Computational Linguistics - Human Language Technologies, 2021 [Download].

[133] W. Ji, J. Li, S. Yu, M. Zhang, Y. Piao, S. Yao, Q. Bi, K. Ma, Y. Zheng, H. Lu, and L. Cheng, “Calibrated RGB-D Salient Object Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2021 [Download].

[132] W. Ji, S. Yu, J. Wu, K. Ma, C. Bian, Q. Bi, J. Li, H. Liu, L. Cheng, and Y. Zheng, “Learning Calibrated Medical Image Segmentation via Multi-rater Agreement Modeling,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2021 [Download].

[131] H. Wang, Z. Yue, Q. Xie, Q. Zhao, Y. Zheng, and D. Meng, “From Rain Generation to Rain Removal,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2021 [Download].

[130] H.-Y. Zhou, H. Liu, S. Cao, D. Wei, C. Lu, Y. Yu, and Y. Zheng, “Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation,” Proc. Int’l Conf. Information Processing in Medical Imaging, 2021 [Download].

[129] Y. Li, J. Chen, K. Ma, and Y. Zheng, “Feature Library: A Benchmark for Cervical Lesion Segmentation,” Proc. Int’l Conf. Information Processing in Medical Imaging, 2021 [Download].

[128] M. Ning, Y. Wang, Y. Guo, C. Bian, C. Yuan, K. Ma, D. Wei, and Y. Zheng, “A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework,” Proc. Int’l Conf. Information Processing in Medical Imaging, 2021 [Download].

[127] S. Wang, R. Zhao, X. Chen, Y. Zheng, and B. Liu, “Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion,” Proc. the Web Conference, 2021 [Download].

[126] Z. Wang, R. Wen, X. Chen, S. Cao, S.-L. Huang, B. Qian, and Y. Zheng, “Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks,” Proc. the Web Conference, 2021 [Download].

[125] S. Wang, S. Cao, D. Wei, C. Xie, K. Ma, L. Wang, D. Meng, and Y. Zheng, “Alternative Baselines for Low-Shot 3D Medical Image Segmentation---An Atlas Perspective,” Proc. AAAI Conf. Artificial Intelligence, 2021 [Download].

[124] G. Qi, L. Gong, Y. Song, K. Ma, and Y. Zheng, “Stabilized Medical Image Attacks,” Proc. Int’l Conf. Learning Representations, 2021 [Download].

[123] Z. Ye, R. Wen, X. Chen, Y. Liu, Z. Zhang, Z. Li, K. Nai, and Y. Zheng, “Improving Short Text Classification Using Context-Sensitive Representations and Content-Aware Extended Topic Knowledge,” Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2021 [Download].

[122] Z. Wang, Y. Yang, R. Wen, X. Chen, S.-L. Huang, and Y. Zheng, “Lifelong Learning based Disease Diagnosis on Clinical Notes,” Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2021 (Best Student Paper Award) [Download].

[121] J. Cao, Y. Xiang, Y. Zhang, Z. Qi, X. Chen, and Y. Zheng, “CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse,” Proc. Int’l Workshop on Semantic Evaluation, 2021 [Download].

[120] Y. Xiang, Z. Zhang, J. Chen, X. Chen, Z. Lin, and Y. Zheng, “OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding,” Findings of Annual Conf. Association for Computational Linguistics, 2021 [Download].

[119] Y. Zhu, C. Markos, R. Zhao, Y. Zheng, and J.Q. Yu, “FedOVA: One-vs-All Training Method for Federated Learning with Non-IID Data,” Proc. Int’l Joint Conf. Neural Networks, 2021 [Download].

[118] C. Yuan, X. Tao, C. Bian, Y. Li., K. Ma, D. Ni, and Y. Zheng, “The Winner of AGE Challenge: Going One Step Further from Keypoint Detection to Scleral Spur Localization,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2021 [Download].

[117] C. Xie, H. Liu, S. Cao, D. Wei., K. Ma, L. Wang, and Y. Zheng, “Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2021 [Download].

[116] C. Xie, S. Cao, D. Wei., H.-Y. Zhou, K. Ma, X. Zhang, B. Qian, L. Wang, and Y. Zheng, “RECIST-Net: Lesion Detection via Grouping Keypoints on RECIST-based Annotation,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2021 [Download].

[115] M. Ning, D. Wei, D. Lu, S. Yu, K. Ma, C. Yuan, C. Bian, and Y. Zheng, “Multi-anchor Active Domain Adaptation for Semantic Segmentation,” Proc. Int’l Conf. Computer Vision, 2021 [Download].

[114] Z. Wang, X. Chen, R. Wen, S.-L. Huang, E. Kuruoglu, and Y. Zheng, “Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback,” Advances in Neural Information Processing Systems, 2020 [Download].

[113] Y. Li, J. Chen, K. Ma, and Y. Zheng, “A Multi-task Self-supervised Learning Framework for Scopy Images,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2020 [Download].

[112] B. Xin, Y. Hu, Y. Zheng, and H. Liao, “Multi-modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2020 [Download].

[111] D. Wei, Y. Li, Y. Wang, T. Qian, and Y. Zheng, “Deep Convolutional Neural Networks for Molecular Subtyping of Gliomas using Magnetic Resonance Imaging,” Proc. SPIE Conf. Medical Imaging, 2020 [Download].

[110] X. Xie, J. Chen, Y. Li, L. Shen, K. Ma, and Y. Zheng, “Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation,” Proc. European Conf. Computer Vision, 2020 [Download].

[109] J. Zhao, D. Lu, K. Ma, Y. Zhang, and Y. Zheng, “Deep Image Clustering with Category-Style Representation,” Proc. European Conf. Computer Vision, 2020 [Download].

[108] S. Wang, Y. Li, K. Ma, R. Ma, H. Guan, and Y. Zheng, “Dual Adversarial Network for Deep Active Learning,” Proc. European Conf. Computer Vision, 2020 [Download].

[107] L. Liu, K. Ma, and Y. Zheng, “Learning Crisp Edge Detector Using Logical Refinement Network,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[106] M. Ning, C. Bian, D. Lu, H.-Y. Zhou, S. Yu, C. Yuan, Y. Guo, Y. Wang, K. Ma, and Y. Zheng, “A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[105] S. Yu, H.-Y. Zhou, K. Ma, C. Bian, C. Chu, H. Liu, and Y. Zheng, “Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[104] J. Wu, S. Yu, W. Chen, K. Ma, R. Fu, H. Liu, X. Di, and Y. Zheng, “Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[103] W. Chen, S. Yu, J. Wu, K. Ma, C. Bian, C. Chu, L. Shen, and Y. Zheng, “TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[102] L. Gong, K. Ma, and Y. Zheng, “Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[101] S. Liu, L. Gong, K. Ma, and Y. Zheng, “GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[100] D. Wei, S. Cao, K. Ma, and Y. Zheng, “Learning and Exploiting Interclass Visual Correlations for Medical Image Classification,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[99] H. Li, D. Wei, S. Cao, K. Ma, L. Wang, and Y. Zheng, “Superpixel-Guided Label Softening for Medical Image Segmentation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[98] H.-Y. Zhou, S. Yu, C. Bian, Y. Hu, K. Ma, and Y. Zheng, “Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs by Comparing Image Representations,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[97] X. Tao, Y. Li, W. Zhou, K. Ma, and Y. Zheng, “Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[96] Y. Li, J. Chen, X. Xie, K. Ma, and Y. Zheng, “Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[95] X. Xie, J. Chen, Y. Li, L. Shen, K. Ma, and Y. Zheng, “Instance-aware Self-supervised Learning for Nuclei Segmentation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[94] X. Xie, J. Chen, Y. Li, L. Shen, K. Ma, and Y. Zheng, “MI2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2020 [Download].

[93] X. Zhang, B. Qian, S. Cao, Y. Li, H. Chen, Y. Zheng, and I. Davidson, “INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare,” ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020 [Download].

[92] Q. Zhang, L. Liu, K. Ma, C. Zhuo, and Y. Zheng, “Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift,” Proc. Int’l Joint Conf. Artificial Intelligence, 2020 [Download].

[91] S. Wang, S. Cao, D. Wei, R. Wang, K. Ma, L. Wang, D. Meng, and Y. Zheng, “LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2020 [Download].

[90] J. Chen, Y. Li, K. Ma, and Y. Zheng, “VideoGAN: Generative Adversarial Networks for Video Domain Adaptation,” Proc. AAAI Conf. Artificial Intelligence, 2020 [Download].

[89] Z. Zhang, H. Liu, J. Chen, X. Chen, B. Liu, Y. Xiang, and Y. Zheng, “An Industry Evaluation of Embedding-based Entity Alignment,” Proc. Int’l Conf. Computational Linguistics, 2020 [Download].

[88] S. Asgari, A. Bentaieb, A. Sharma, S. K. Zhou, Y. Zheng, B. Georgescu, P. Sharma, S. Grbic, Z. Xu, D. Comaniciu, and G. Hamarneh, “Select, Attend, and Transfer: Light, Learnable Skip Connections,” Proc. MICCAI Workshop on Machine Learning in Medical Imaging, 2019 [Download].

[87] R. Wang, S. Cao, K. Ma, D. Meng, and Y. Zheng, “Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2019 [Download].

[86] Q. Shao, L. Gong, K. Ma, H. Liu, and Y. Zheng, “Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2019 [Download].

[85] W. Ma, S. Yu, K. Ma, J. Wang, X. Ding, and Y. Zheng, “Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2019 [Download].

[84] X. Zhuang, Y. Li, Y. Hu, K. Ma, Y. Yang, and Y. Zheng, “Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2019 [Download].

[83] X. Ying, H. Guo, K. Ma, J. Wu, Z. Weng, and Y. Zheng, “X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2019 [Download].

[82] Y. Chen, J. Chen, D. Wei, Y. Li, and Y. Zheng, “OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images,” Proc. MICCAI Workshop on Multiscale Multimodal Medical Imaging, 2019 (Best Paper Award) [Download].

[81] C. Bian, X. Yang, S. Zheng, Y.-A. Liu, R. Nezafat, P.-A. Heng, and Y. Zheng, “Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation,” MICCAI Workshop on STACOM, 2018 [Download].

[80] H. Liao, G. Funka-Lea, Y. Zheng, J. Luo, and S. Kevin Zhou, “Face Completion with Semantic Knowledge and Collaborative Adversarial Learning,” Proc. Asian Conf. Computer Vision, 2018 [Download].

[79] Z. Zhang, L. Yang, and Y. Zheng, “Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2018 [Download].

[78] H. Liao, G. Funka-Lea, Y. Zheng, J. Luo, and K. S. Zhou, “More Knowledge is Better: Cross-modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2018

[77] P. Zhang, F. Wang, and Y. Zheng, “Self Supervised Deep Representation Learning for Fine-Grained Body Part Recognition,” Proc. IEEE Int'l Sym. Biomedical Imaging, 2017 [Download].

[76] A. Hoogi, J. W. Lambert, Y. Zheng, D. Comaniciu, and D. L. Rubin, “A Fully Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes,” NIPS Workshop on Machine Learning for Health, 2016 [Download].

[75] M. A. Gulsun, G. Funka-Lea, P. Sharma, S. Rapaka, and Y. Zheng, “Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2016 [Download].

[74] H. Chen, Y. Zheng, J.-H. Park, P. A. Heng, and S. K. Zhou, “Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2016 [Download].

[73] P. Chu, Y. Pang, E. Cheng, Y. Zhu, Y. Zheng, and H. Ling, “Structure-Aware Rank-1 Tensor Approximation for Curvilinear Structure Tracking Using Learned Hierarchical Features,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2016 [Download].

[72] S. Miao, Z. J. Wang, Y. Zheng, and R. Liao, “Real-Time 2D/3D Registration via CNN Regression,” Proc. IEEE Int'l Sym. Biomedical Imaging, 2016 [Download].

[71] X. Cheng, L. Zhang, and Y. Zheng, “Deep Similarity Learning for Multimodal Medical Images,” Proc. MICCAI Workshop on Deep Learning in Medical Image Analysis, 2015 [Download].

[70] F. C. Ghesu, B. Georgescu, Y. Zheng, J. Hornegger, and D. Comaniciu, “Marginal Space Deep Learning: Efficient Architecture for Detection in Volumetric Image Data,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2015 [Download].

[69] Y. Zheng, D. Liu, B. Georgescu, H. Nguyen, and D. Comaniciu, “3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2015 [Download].

[68] Y. Zheng, “Cross-Modality Medical Image Detection and Segmentation by Transfer Learning of Shape Priors,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2015 [Download].

[67] M. Chen, H. Zhong, Y. Zheng, and G. Funka-Lea, “Component-Composition Based Heart Isolation for 3D Volume Visualization of Coronary Arteries,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2015 [Download].

[66] F. Lugauer, Y. Zheng, J. Hornegger, and B. K. Kelm, “Precise Lumen Segmentation in Coronary Computed Tomography Angiography,” MICCAI Workshop on Medical Computer Vision, 2014 [Download].

[65] B. M. Kelm and Y. Zheng, “Automatic Coronary Calcium Scoring Using Native and Contrasted CT Acquisitions,” MICCAI Challenge on Automatic Coronary Calcium Scoring, 2014 [Download].

[64] M. A. Gulsun, G. Funka-Lea, Y. Zheng, and M. Eckert, “CTA Coronary Labeling Through Efficient Geodesics Between Trees and Anatomy Priors,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2014 [Download].

[63] Y. Zheng, “Pericardium Based Model Fusion of CT and Non-Contrasted C-arm CT for Visual Guidance in Cardiac Interventions,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2014 [Download].

[62] S. Lu, X. Huang, Z. Wang, and Y. Zheng, “Sparse Appearance Learning Based Automatic Coronary Sinus Segmentation in CTA,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2014 [Download].

[61] F. Lugauer, J. Zhang, Y. Zheng, J. Hornegger, and M. Kelm, “Improving Accuracy in Coronary Lumen Segmentation via Explicit Calcium Exclusion, Learning-based Ray Detection and Surface Optimization,” Proc. SPIE Conf. Medical Imaging, 2014 [Download].

[60] A. G. Schwing and Y. Zheng, “Reliable Extraction of the Mid-Sagittal Plane in 3D Brain MRI via Hierarchical Landmark Detection,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2014 [Download].

[59] L. Lu, P. Devarakota, S. Vikal, D. Wu, Y. Zheng, and M. Wolf, “Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation,” Proc. Workshop on Medical Computer Vision (In conjunction with MICCAI), 2013 [Download].

[58] M. Chen, Y. Zheng, Y. Wang, K. Mueller, and G. Lauritsch, “Automatic 3D Motion Estimation of Left Ventricle from C-arm Rotational Angiocardiography Using Prior Motion Model and Learning Based Boundary Detector,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2013 [Download].

[57] Y. Zheng, H. Tek, and G. Funka-Lea, “Robust and Accurate Coronary Artery Centerline Extraction in CTA by Combining Model-Driven and Data-Driven Approaches,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2013 [Download].

[56] H. Zhong, Y. Zheng, G. Funka-Lea, and F. Vega-Higuera, “Automatic Heart Isolation in 3D CT Images,” Medical Computer Vision, Edited by B. Menze, G. Langs, L. Lu, A. Montillo, Z. Tu, and A. Criminisi, Springer, 2013, pp. 165-180, (ISBN: 978-3-642-36619-2) [Download].

[55] D. Yang, Y. Zheng, and M. John, “Graph Cuts Based Left Atrium Segmentation Refinement and Right Middle Pulmonary Vein Extraction in C-arm CT,” Proc. SPIE Conf. Medical Imaging, 2013, pp. 1-9 [Download].

[54] C. Lu, Y. Zheng, N. Birkbeck, J. Zhang, T. Boettger, C. Tietjen, J. S. Duncan, and S. K. Zhou, “Precise Segmentation of Multiple Organs in CT Volumes Using Learning-based Approach and Information Theory,”Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention , 2012 [Download].

[53] M. Chen, J. Bai, Y. Zheng, and R. A. C. Siochi, “3D Lung Tumor Motion Model Extraction from 2D Projection Images of Mega-Voltage Cone Beam CT via Optimal Graph Search,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2012 [Download].

[52] P. Wang, Y. Zheng, M. John, and D. Comaniciu, “Catheter Tracking via Online Learning for Dynamic Motion Compensation in Transcatheter Aortic Valve Implantation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2012 [Download].

[51] Y. Zheng, J. Shen, H. Tek, and G. Funka-Lea, “Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries,” Proc. Int’l Workshop on Machine Learning in Medical Imaging (In conjunction with MICCAI), 2012 [Download].

[50] H. Zhong, Y. Zheng, G. Funka-Lea, and F. Vega-Higuera, “Segmentation and Removal of Pulmonary Arteries, Veins and Left Atrial Appendage for Visualizing Coronary and Bypass Arteries,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2012 [Download].

[49] K. Muller, Y. Zheng, G. Lauritsch, C. Rohkohl, C. Schwemmer, A. K. Maier, R. Fahrig, and J. Hornegger, “Evaluation of Interpolation Methods for Motion Compensated Tomographic Reconstruction for Cardiac Angiographic C-arm Data,” Proc. The Second International Conference on Image Formation in X-Ray Computed Tomography, 2012 [Download].

[48] D. Vitanovski, K. Ralovich, R. Ionasec, Y. Zheng, M. Suehling, W. Krawtschuk, J. Hornegger, and Dorin Comaniciu, “Personalized Learning-based Segmentation of Thoracic Aorta and Main Branches for Diagnosis and Treatment Planning,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2012 [Download].

[47] M. Chen, Y. Zheng, K. Mueller, C. Rohkohl, G. Lauritsch, J. Boese, and D. Comaniciu, “Enhancement of Organ of Interest via Background Subtraction in Cone Beam Rotational Angiogram,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2012 [Download].

[46] Y. Zheng, M. John, J. Boese, and D. Comaniciu, “Precise Segmentation of the Left Atrium in C-arm CT Volumes with Applications to Atrial Fibrillation Ablation,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2012 [Download].

[45] S. Tzoumas, P. Wang, Y. Zheng, M. John, and D. Comaniciu, “Robust Pigtail Catheter Tip Detection in Fluoroscopy,” Proc. SPIE Conf. Medical Imaging, 2012 [Download].

[44] H. Tek, Y. Zheng, M. A. Gulsun, and G. Funka-Lea, “An Automatic System for Segmenting Coronary Arteries from CTA,”Proc. MICCAI Workshop on Computing and Visualization for Intravascular Imaging,2011 [Download].

[43] S. Grbic, R. Ionasec, T. Mansi, Y. Wang, B. Georgescu, M. John, J. Boese, Y. Zheng, N. Navab, and D. Comaniciu, “Model-Based Fusion of Multi-Modal Volumetric Images: Application to Transcatheter Valve Procedures,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2011 [Download].

[42] M. Chen, Y. Zheng, K. Mueller, C. Rohkohl, G. Lauritsch, J. Boese, G. Funka-Lea, and D. Comaniciu, “Automatic Extraction of 3D Dynamic Left Ventricle Model from 2D Rotational Angiography,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2011[Download].

[41] M. Kelm, S. Mittal, Y. Zheng, G. Funka-Lea, D. Bernhardt, F. Vega-Higuera, and D. Comaniciu, “Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2011 [Download].

[40] Y. Zheng, T. Wang, M. John, S. K. Zhou, J. Boese, D. Comaniciu, “Multi-Part Left Atrium Modeling and Segmentation in C-Arm CT Volumes for Atrial Fibrillation Ablation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2011 [Download].

[39] Y. Zheng, H. Tek, G. Funka-Lea, S. K. Zhou, F. Vega-Higuera, and D. Comaniciu, “Efficient Detection of Native and Bypass Coronary Ostia in Cardiac CT Volumes: Anatomical vs. Pathological Structures,”Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2011 [Download].

[38] A.G. Schwing, C. Zach, Y. Zheng and M. Pollefeys, “Adaptive Random Forest - How many ``experts'' to ask before making a decision?” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011 [Download].

[37] S. Miao, R. Liao, and Y. Zheng, “A Hybrid Method for 2-D/3-D Registration Between 3-D Volumes and 2-D Angiography for Trans-Catheter Aortic Valve Implantation (TAVI),” Proc. IEEE Int’l Sym. Biomedical Imaging, 2011 [Download].

[36] Y. Zheng, M. Loziczonek, B. Georgescu, S. K. Zhou, F. Vega-Higuera, and D. Comaniciu, “Machine Learning Based Vesselness Measurement for Coronary Artery Segmentation in Cardiac CT Volumes,” Proc. SPIE Conf. Medical Imaging, 2011 [Download].

[35] B. M. Kelm, S. K. Zhou, M. Suehling, Y. Zheng, M. Wels, and D. Comaniciu, “Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning,” Proc. Int’l Workshop on Medical Computer Vision -- Recognition Techniques and Applications in Medical Imaging (In conjunction with MICCAI),2010 (Best Paper Award) [Download].

[34] R. Ionasec, I. Voigt, V. Mihalef, S. Grbic, D. Vitanovski, Y. Wang, Y. Zheng, J. Hornegger, N. Navab, B. Georgescu, and D. Comaniciu, “Patient-specific Modeling of the Heart: Applications to Cardiovascular Disease Management,” Proc. Int’l Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function (In conjunction with MICCAI), 2010 [Download].

[33] S. Mittal, Y. Zheng, B. Georgescu, F. Vega-Higuera, S. K. Zhou, P. Meer, and D. Comaniciu, “Fast Automatic Detection of Calcified Coronary Lesions in 3D Cardiac CT Images,” Proc. Int’l Workshop on Machine Learning in Medical Imaging (In conjunction with MICCAI), 2010 [Download].

[32] Y. Zheng, F. Vega-Higuera, S. K. Zhou, and D. Comaniciu, “Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization,” Proc. Int’l Workshop on Machine Learning in Medical Imaging (In conjunction with MICCAI), 2010 [Download].

[31] M. John, R. Liao, Y. Zheng, A. Nottling, J. Boese, U. Kirschstein, J. Kempfert, and T. Walther, “System to Guide Transcatheter Aortic Valve Implantations Based on Interventional 3D C-arm CT Imaging,”Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2010 [Download].

[30]Y. Zheng, M. John, R. Liao, J. Boese, U. Kirschstein, B. Georgescu, S. K. Zhou, J. Kempfert, T. Walther, G. Brockmann, and D. Comaniciu, “Automatic Aorta Segmentation and Valve Landmark Detection in C-Arm CT: Application to Aortic Valve Implantation,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2010 [Download].

[29] V. Mihalef, R. I. Ionasec, Y. Wang, Y. Zheng, B. Georgescu, and D. Comaniciu, “Patient-Specific Modeling of Left Heart Anatomy, Dynamics and Hemodynamics from High Resolution 4D CT,” Proc. IEEE Int’l Sym. Biomedical Imaging, 2010  [Download].

[28] S. Grbic, R. I. Ionasec, Y. Zheng, D. Zauner, B. Georgescu, and D. Comaniciu, “Aortic Valve and Ascending Aortic Root Modeling from 3D and 3D+t CT,” Proc. SPIE Conf. Medical Imaging, 2010  [Download].

[27]M. Wels, Y. Zheng, G. Carneiro, M. Huber, J. Hornegger, and D. Comaniciu, "Fast and Robust 3-D MRI Brain Structure Segmentation,"Proc. Int'l Conf. Medical Image Computing and Computer Assisted Intervention, 2009 [Download].

[26] Y. Zheng, B. Georgescu, and D. Comaniciu, "Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images,"Proc. Information Processing in Medical Imaging, 2009 [Download].

[25] Y. Zheng, B. Georgescu, H. Ling, K.S. Zhou, M. Scheuering, and D. Comaniciu, "Constrained Marginal Space Learning for Efficient 3D Anatomical Structure Detection in Medical Images," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009 [Download].

[24] Y. Zheng, X. Lu, B. Georgescu, A. Littmann, E. Mueller, and D. Comaniciu, "Robust Object Detection Using Marginal Space Learning and Ranking-Based Multi-Detector Aggregation: Application to Automatic Left Ventricle Detection in 2D MRI Images," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009 [Download].

[23] Y. Zheng, X. Lu, B. Georgescu, A. Littmann, E. Mueller, and D. Comaniciu, "Automatic Left Ventricle Detection in MRI Images Using Marginal Space Learning and Component-Based Voting," Proc. SPIE Medical Imaging, 2009[Download].

[22] Y. Zheng, B. Georgescu, F. Vega-Higuera, and D. Comaniciu, "Left Ventricle Endocardium Segmentation for Cardiac CT Volumes Using an Optimal Smooth Surface," Proc. SPIE Medical Imaging, 2009 [Download].

[21] G. Zhu, Y. Zheng, and D. Doermann, "Signature-based Document Image Retrieval," Proc. European Conf. Computer Vision, 2008 [Download].

[20] X. Lu, B. Georgescu, Y. Zheng, J. Otsuki, R. Bennett, and D. Comaniciu, "Automatic Detection of Standard Planes from Three Dimensional Echocardiographic Data," Proc. IEEE Int’l Sym. Biomedical Imaging, 2008 [Download].

[19] L. Yang, B. Georgescu, Y. Zheng, D. J. Foran, and D. Comaniciu, "A Fast and Accurate Tracking Algorithm of Left Ventricles in 3D  Echocardiography," Proc. IEEE Int’l Sym. Biomedical Imaging, 2008 [Download].

[18] L. Yang, B. Georgescu, Y. Zheng, P. Meer, and D. Comaniciu, "3D Ultrasound Tracking of the Left Ventricles Using One-Step Forward Prediction and Data Fusion of Collaborative Trackers," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008 [Download].

[17] H. Ling, S.K. Zhou, Y. Zheng, B. Georgescu,M. Suehling, and D. Comaniciu, "Hierarchical, Learning-based Automatic Liver Segmentation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008 [Download].

[16] Y. Zheng, B. Georgescu, A. Barbu, M. Scheuering, and D. Comaniciu, "Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes," Proc. SPIE Conf. Medical Imaging, 2008 [Download].

[15] Y. Zheng, A. Barbu, B. Georgescu, M. Scheuring, and D. Comaniciu, "Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features,"Proc. Int'l Conf. Computer Vision, 2007 [Download] .

[14] G. Zhu, Y. Zheng, D. Doermann, and S. Jaeger, "Multi-scale Structural Saliency for Signature Detection,"Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007, pp. 1-8 [Download] .

[13] Y. Li, Y. Zheng, and D. Doermann, "Detecting Text Lines in Handwritten Documents," Proc. Int’l Conf. Pattern Recognition, pp. 1030-1033, 2006[Download] .

[12] Y. Li, Y. Zheng, D. Doermann, and S. Jaeger, "A New Algorithm for Detecting Text Line in Handwritten Documents,"Proc.Int’l Workshop on Frontiers in Handwriting Recognition, 2006 (Best Student Paper Award) [Download] .

[11] Y. Zheng, X. S. Zhou, B. Georgescu, S.K. Zhou, and D. Comaniciu, "Example Based Non-rigid Shape Detection," Proc. European Conf. Computer Vision, May 2006: 423-436.  [Download]

[10] Y. Zheng and D. Doermann, "Handwriting Matching and Its Application to Handwriting Synthesis," Proc. Int’l Conf. Document Analysis &  Recognition, 2005: 861-865[Download].

[9] Y. Zheng and D. Doermann, "Robust Point Matching for Two-Dimensional Non-Rigid Shapes," Proc. Int’l Conf. Computer Vision, 2005, pp. 1561-1566.  [Download]

[8] Y. Zheng, H. Li, D. Doermann, "Text Identification in Noisy Document Images Using Markov Random Field," Proc. Int'l Conf. Document Analysis & Recognition, 2003, pp. 599-603. [Download]

[7] Y. Zheng, H. Li, D. Doermann, "A Model-based Line Detection Algorithm in Documents," Proc. Int'l Conf. Document Analysis & Recognition,  2003, pp. 44-48. [Download]

[6] Y. Zheng, H. Li, D. Doermann, "Processing Noisy Documents," Symposium on Document Image Understanding Technology, 2003, pp. 97-109. [Download] 

[5] Y. Zheng, H. Li, D. Doermann, "Background Line Detection with A Stochastic Model," IEEE Workshop on Document Image Analysis and Retrieval (In conjunction with IEEE CVPR'03), 2003. [Download]

[4] M. Chen, M. Wu, Y. Zheng, "Classification-based Error Concealment for Images," Proc. Int'l Conf. Image Processing, 2003. pp. 675-678. [Download]

[3] Y. Zheng, H. Li, and D. Doermann, "Segmentation and Identification of Handwriting in Noisy Documents," Proc. of IAPR Conf. Document Analysis System, 2002, pp. 95-105. [Download]

[2] Y. Zheng, C. Liu, and X. Ding, "Single Character Type Identification," Proc. of SPIE Document Recognition and Retrieval IX, Vol. 4670, 2002, pp. 49-56. [Download]

[1] Y. Zheng, C. Liu, X. Ding, and S. Pan, "Form Frame Line Detection with Directional Single-Connected Chain," Proc. of Int'l Conf. Document Analysis & Recognition, 2001, pp. 699-703. [Download]