Qian WANG | 王 乾

Conference Paper

  1. H. Xiong, Y. Fang, K. Sun, Y. Wang, X. Zong, W. Zhang, and Q. Wang, “Contrast Representation Learning from Imaging Parameters for Magnetic Resonance Image Synthesis,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, Marrakesh, Morocco, 2024, pp. 181–190.
  2. F. Li, Z. Zhou, Y. Fang, J. Cai, and Q. Wang, “MoCo-Diff: Adaptive Conditional Prior on Diffusion Network for MRI Motion Correction,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, Marrakesh, Morocco, 2024, pp. 411–421. 
  3. H. Huang, Z. Shen, J. Wang, X. Wang, J. Lu, H. Lin, J. Ge, C. Zuo, and Q. Wang, “MetaAD: Metabolism-Aware Anomaly Detection for Parkinson’s Disease in 3D 18F-FDG PET,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, Marrakesh, Morocco, 2024, pp. 291–301. (Winner of MICCAI Young Scientist Award)
  4. M. Liu, Z. Song, D. Chen, X. Wang, Z. Zhuang, M. Fei, L. Zhang, and Q. Wang, “Affinity Learning Based Brain Function Representation for Disease Diagnosis,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, Marrakesh, Morocco, 2024, pp. 14–23.
  5. Y. Kong, S. Wang, J. Cai, Z. Zhao, Z. Shen, Y. Li, M. Fei, and Q. Wang, “Gaze-DETR: Using Expert Gaze to Reduce False Positives in Vulvovaginal Candidiasis Screening,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, Marrakesh, Morocco, 2024, pp. 133–143.
  6. D. Chen, L. Yao, M. Liu, Z. Shen, Y. Hu, Z. Song, Q. Wang, and L. Zhang, “Self-supervised Learning with Adaptive Graph Structure and Function Representation For Cross-Dataset Brain Disorder Diagnosis,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, Marrakesh, Morocco, 2024, pp. 612–622.
  7. X. Wang, Z. Song, Y. Zhu, S. Wang, L. Zhang, D. Shen, and Q. Wang, “Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning,” in ISBI 2024, Athens, Greece, 2024
  8. Y. Zhu, Z. Shen, Z. Zhao, S. Wang, X. Wang, X. Zhao, D. Shen, and Q. Wang, “MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis,” in ISBI 2024, Athens, Greece, 2024
  9. L. Chen, L. Yao, Q. Wang, and Z. Xue, “Semi-Supervised Learning of Visual Attributes for Automated Assessment of Lung Nodule Malignancy,” in ISBI 2024, Athens, Greece, 2024
  10. Z. Zhao, S. Wang, Q. Wang, and D. Shen, “Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis,” in AAAI Conference on Artificial Intelligence, Vancouver, British Columbia, Canada, 2024, vol. 38, pp. 7543–7551.
  11. S. Wang, Z. Zhao, L. Zhang, D. Shen, and Q. Wang, “Crafting Good Views of Medical Images for Contrastive Learning via Expert-level Visual Attention,” in The 2nd Gaze Meets ML Workshop, New Orleans, Louisiana, United States, 2024.
  12. J. Cai, H. Xiong, M. Cao, L. Liu, L. Zhang, and Q. Wang, “Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, British Columbia, Canada, 2023, pp. 233–242.
  13. M. Cao, M. Fei, J. Cai, L. Liu, L. Zhang, and Q. Wang, “Detection-free Pipeline for Cervical Cancer Screening of Whole Slide Images,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, British Columbia, Canada, 2023, pp. 243–252.
  14. M. Fei, X. Zhang, M. Cao, Z. Shen, X. Zhao, Z. Song, Q. Wang, and L. Zhang, “Robust Cervical Abnormal Cell Detection via Distillation from Local-scale Consistency Refinement,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, British Columbia, Canada, 2023, pp. 652–661.
  15. Z. Shen, M. Cao, S. Wang, L. Zhang, and Q. Wang, “CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, British Columbia, Canada, 2023, pp. 487–496.
  16. Z. Song, X. Wang, X. Zhao, S. Wang, Z. Shen, Z. Zhuang, M. Liu, Q. Wang, and L. Zhang, “Alias-Free Co-Modulated Network for Cross-Modality Synthesis and Super-Resolution of MR Images,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, British Columbia, Canada, 2023, pp. 66–76.
  17. Z. Zhuang, X. Wang, S. Wang, Z. Shen, X. Zhao, M. Liu, Z. Xue, D. Shen, L. Zhang, and Q. Wang, “CAS-Net: Cross-view Aligned Segmentation by Graph Representation of Knees,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, British Columbia, Canada, 2023, pp. 110–119.
  18. X. Zhao, Z. Shen, D. Chen, S. Wang, Z. Zhuang, Q. Wang, and L. Zhang, “One-Shot Traumatic Brain Segmentation with Adversarial Training and Uncertainty Rectification,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, British Columbia, Canada, 2023, pp. 120–129.
  19. D. Chen, M. Liu, Z. Shen, X. Zhao, Q. Wang, and L. Zhang, “Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cognitive Disorder Diagnosis,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, British Columbia, Canada, 2023, pp. 56–66.
  20. X. Wang, Z. Shen, Z. Song, S. Wang, M. Liu, L. Zhang, K. Xuan, and Q. Wang, “Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion,” in Machine Learning in Medical Imaging, Vancouver, British Columbia, Canada, 2023, pp. 23–32.
  21. Z. Zhuang, S. Wang, L. Si, K. Xuan, Z. Xue, D. Shen, L. Zhang, W. Yao, and Q. Wang, “Local Graph Fusion of Multi-View MR Images for Knee Osteoarthritis Diagnosis,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Singapore, 2022, pp. 554–563.
  22. X. Zhang, M. Cao, S. Wang, J. Sun, X. Fan, Q. Wang, and L. Zhang, “Whole Slide Cervical Cancer Screening Using Graph Attention Network and Supervised Contrastive Learning,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Singapore, 2022, pp. 202–211.
  23. M. Cao, X. Zhang, X. Fan, L. Zhang, and Q. Wang, “Parallel Classification of Cells in Thinprep Cytology Test Image for Cervical Cancer Screening,” in Cancer Prevention Through Early Detection, Singapore, 2022, pp. 34–43.
  24. L. Yao, Z. Xue, Y. Zhan, L. Chen, Y. Chen, B. Song, Q. Wang, F. Shi, and D. Shen, “Head and Neck Vessel Segmentation with Connective Topology Using Affinity Graph,” in Machine Learning in Medical Imaging, Singapore, 2022, pp. 230–238.
  25. L. Yao, Z. Xue, Y. Zhan, L. Chen, Y. Chen, B. Song, Q. Wang, F. Shi, and D. Shen, “TaG-Net: Topology-Aware Graph Network for Vessel Labeling,” in Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis, Singapore, 2022, pp. 108–117.
  26. Z. Shen, X. Ouyang, Z. Wang, Y. Zhan, Z. Xue, Q. Wang, J.-Z. Cheng, and D. Shen, “Nodule Synthesis and Selection for Augmenting Chest X-ray Nodule Detection,” in Pattern Recognition and Computer Vision, Beijing, China, 2021, pp. 536–547.
  27. X. Ouyang, J. Che, Q. Chen, Z. Li, Y. Zhan, Z. Xue, Q. Wang, J.-Z. Cheng, and D. Shen, “Self-adversarial Learning for Detection of Clustered Microcalcifications in Mammograms,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, Strasbourg, France, 2021, pp. 78–87.
  28. Y. Qiao, H. Tao, J. Huo, W. Shen, Q. Wang, and L. Zhang, “Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance,” in Machine Learning in Clinical Neuroimaging, Strasbourg, France, 2021, pp. 92–100.
  29. S. Wang, Z. Zhuang, K. Xuan, D. Qian, Z. Xue, J. Xu, Y. Liu, Y. Chai, L. Zhang, Q. Wang, and D. Shen, “3DMeT: 3D Medical Image Transformer for Knee Cartilage Defect Assessment,” in Machine Learning in Medical Imaging, Strasbourg, France, 2021, pp. 347–355.
  30. W. Shen, L. Chen, D. Wei, Y. Qiao, Y. Zhan, D. Shen, and Q. Wang, “A Recurrent Two-Stage Anatomy-Guided Network for Registration of Liver DCE-MRI,” in Machine Learning in Medical Imaging, Strasbourg, France, 2021, pp. 219–227.
  31. Z. Wang, T. Huang, B. Xiao, J. Huo, S. Wang, H. Jiang, H. Liu, F. Wu, X. Zhou, Z. Xue, J. Yang, and Q. Wang, “Self-guided Multi-attention Network for Periventricular Leukomalacia Recognition,” in Predictive Intelligence in Medicine, Strasbourg, France,, 2021, pp. 128–137.
  32. X. Du, J. Huo, Y. Qiao, Q. Wang, and L. Zhang, “False Positive Suppression in Cervical Cell Screening via Attention-Guided Semi-supervised Learning,” in Predictive Intelligence in Medicine, Strasbourg, France, 2021, pp. 93–103.
  33. H. Tao, Y. Qiao, L. Zhang, Y. Zhan, Z. Xue, and Q. Wang, “Anatomical Structure-Aware Pulmonary Nodule Detection via Parallel Multi-task RoI Head,” in Predictive Intelligence in Medicine, Strasbourg, France, 2021, pp. 212–220.
  34. K. Xuan, S. Sun, Z. Xue, Q. Wang, and S. Liao, “Learning MRI k-Space Subsampling Pattern Using Progressive Weight Pruning,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Lima, Peru, 2020, pp. 178–187.
  35. L. Chen, X. Cao, L. Chen, Y. Gao, D. Shen, Q. Wang, and Z. Xue, “Semantic Hierarchy Guided Registration Networks for Intra-subject Pulmonary CT Image Alignment,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Lima, Peru, 2020, pp. 181–189.
  36. M. Zhou, L. Zhang, X. Du, X. Ouyang, X. Zhang, Q. Shen, and Q. Wang, “Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening,” in Machine Learning in Medical Imaging, Lima, Peru, 2020, pp. 414–422.
  37. L. Yao, P. Jiang, Z. Xue, Y. Zhan, D. Wu, L. Zhang, Q. Wang, F. Shi, and D. Shen, “Graph Convolutional Network Based Point Cloud for Head and Neck Vessel Labeling,” in Machine Learning in Medical Imaging, Lima, Peru, 2020, pp. 474–483.
  38. B. Xiao, N. He, Q. Wang, Z. Xue, L. Chen, F. Yan, F. Shi, and D. Shen, “Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer,” in Machine Learning in Medical Imaging, Lima, Peru, 2020, pp. 241–249.
  39. S. Wang, J. Huo, X. Ouyang, J. Che, Z. Xue, D. Shen, Q. Wang, and J.-Z. Cheng, “mr2NST: Multi-resolution and Multi-reference Neural Style Transfer for Mammography,” in Predictive Intelligence in Medicine, Lima, Peru, 2020, pp. 169–177.
  40. J. Huo, L. Si, X. Ouyang, K. Xuan, W. Yao, Z. Xue, Q. Wang, D. Shen, and L. Zhang, “A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency,” in Predictive Intelligence in Medicine, Lima, Peru, 2020, pp. 200–209.
  41. T. Li, Z. Wang, Y. Chen, L. Zhang, Y. Gao, F. Shi, D. Qian, Q. Wang, and D. Shen, “Two-Stage Mapping-Segmentation Framework for Delineating COVID-19 Infections from Heterogeneous CT Images,” in Thoracic Image Analysis, Lima, Peru, 2020, pp. 3–13.
  42. S. Chen, S. Sun, X. Huang, D. Shen, Q. Wang, and S. Liao, “Data-Consistency in Latent Space and Online Update Strategy to Guide GAN for Fast MRI Reconstruction,” in Machine Learning for Medical Image Reconstruction, Lima, Peru, 2020, pp. 82–90.
  43. Y. Guo, L. Bi, E. Ahn, D. Feng, Q. Wang, and J. Kim, “A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 4725–4734.
  44. X. Ren, J. Huo, K. Xuan, D. Wei, L. Zhang, and Q. Wang, “Robust Brain Magnetic Resonance Image Segmentation for Hydrocephalus Patients: Hard and Soft Attention,” in IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, pp. 385–389.
  45. M. H. Obiols, Y. Jiao, and Q. Wang, “Can radiomics features boost the performance of deep learning upon histology images?” in International Conference on Medical Imaging Physics and Engineering (ICMIPE), Shenzhen, China, 2019, pp. 1–6.
  46. O. M. Ijurra, Y. Jiao, and Q. Wang, “ConvRadiomics: Convolutional Radiomics Feature Extraction Toolkit,” in International Conference on Medical Imaging Physics and Engineering (ICMIPE), Shenzhen, China, 2019, pp. 1–5.
  47. K. Xuan, D. Wei, D. Wu, Z. Xue, Y. Zhan, W. Yao, and Q. Wang, “Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans Using Sparse Fidelity Loss and Adversarial Regularization,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Shenzhen, China, 2019, pp. 65–73.
  48. X. Ouyang, Z. Xue, Y. Zhan, X. S. Zhou, Q. Wang, Y. Zhou, Q. Wang, and J.-Z. Cheng, “Weakly Supervised Segmentation Framework with Uncertainty: A Study on Pneumothorax Segmentation in Chest X-ray,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Shenzhen, China, 2019, pp. 613–621.
  49. D. Wei, S. Ahmad, J. Huo, W. Peng, Y. Ge, Z. Xue, P.-T. Yap, W. Li, D. Shen, and Q. Wang, “Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Shenzhen, China, 2019, pp. 512–520.
  50. Z. Tang, Y. Xu, Z. Jiao, J. Lu, L. Jin, A. Aibaidula, J. Wu, Q. Wang, H. Zhang, and D. Shen, “Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Shenzhen, China, 2019, pp. 415–422.
  51. P. Huang, D. Li, Z. Jiao, D. Wei, G. Li, Q. Wang, H. Zhang, and D. Shen, “CoCa-GAN: Common-Feature-Learning-Based Context-Aware Generative Adversarial Network for Glioma Grading,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Shenzhen, China, 2019, pp. 155–163.
  52. Y. Guo, L. Bi, A. Kumar, Y. Gao, R. Zhang, D. Feng, Q. Wang, and J. Kim, “Deep Local-Global Refinement Network for Stent Analysis in IVOCT Images,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Shenzhen, China, 2019, pp. 539–546.
  53. D. Wei, S. Ahmad, Z. Wu, X. Cao, X. Ren, G. Li, D. Shen, and Q. Wang, “Morphological Simplification of Brain MR Images by Deep Learning for Facilitating Deformable Registration,” in Machine Learning in Medical Imaging, Shenzhen, China, 2019, pp. 203–211.
  54. B. Xiao, X. Cheng, Q. Li, Q. Wang, L. Zhang, D. Wei, Y. Zhan, X. S. Zhou, Z. Xue, G. Lu, and F. Shi, “Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation,” in Machine Learning in Medical Imaging, Shenzhen, China, 2019, pp. 409–416.
  55. X. Ren, L. Zhang, D. Wei, D. Shen, and Q. Wang, “Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization,” in Machine Learning in Medical Imaging, Shenzhen, China, 2019, pp. 1–8.
  56. Y. Jiao, O. M. Ijurra, L. Zhang, D. Shen, and Q. Wang, “cuRadiomics: A GPU-Based Radiomics Feature Extraction Toolkit,” in Radiomics and Radiogenomics in Neuro-oncology, Shenzhen, China, 2019, pp. 44–52.
  57. Y. Ge, D. Wei, Z. Xue, Q. Wang, X. Zhou, Y. Zhan, and S. Liao, “Unpaired Mr to CT Synthesis with Explicit Structural Constrained Adversarial Learning,” in IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019, pp. 1096–1099.
  58. L. Xiang, Y. Chen, W. Chang, Y. Zhan, W. Lin, Q. Wang, and D. Shen, “Ultra-Fast T2-Weighted MR Reconstruction Using Complementary T1-Weighted Information,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Granada, Spain, 2018, pp. 215–223.
  59. L. Xiang, Y. Li, W. Lin, Q. Wang, and D. Shen, “Unpaired Deep Cross-Modality Synthesis with Fast Training,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain, 2018, pp. 155–164.
  60. X. Cao, J. Yang, L. Wang, Z. Xue, Q. Wang, and D. Shen, “Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity,” in Machine Learning in Medical Imaging, Granada, Spain, 2018, pp. 55–63.
  61. G. Zeng, Q. Wang, T. Lerch, F. Schmaranzer, M. Tannast, K. Siebenrock, and G. Zheng, “Latent3DU-net: Multi-level Latent Shape Space Constrained 3D U-net for Automatic Segmentation of the Proximal Femur from Radial MRI of the Hip,” in Machine Learning in Medical Imaging, Granada, Spain, 2018, pp. 188–196.
  62. L. Zhang, H. Zhang, I. Rekik, Y. Gao, Q. Wang, and D. Shen, “Malignant Brain Tumor Classification Using the Random Forest Method,” in Structural, Syntactic, and Statistical Pattern Recognition, Beijing, China, 2018, pp. 14–21.
  63. S. Chen, W. Tang, J. Hu, Y. Cheng, Q. Wang, X. Wu, and D. Shen, “Longitudinal Sparse Regression for Neuroimage Based Consciousness Assessing and Tracking of Hydrocephalus Patients,” in IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 2018, pp. 595–598.
  64. D. Nie, R. Trullo, J. Lian, C. Petitjean, S. Ruan, Q. Wang, and D. Shen, “Medical Image Synthesis with Context-Aware Generative Adversarial Networks,” in Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, Quebec City, QC, Canada, 2017, pp. 417–425.
  65. L. Chen, H. Zhang, K.-H. Thung, L. Liu, J. Lu, J. Wu, Q. Wang, and D. Shen, “Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients,” in Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, Quebec City, QC, Canada, 2017, pp. 450–458.
  66. X. Cao, J. Yang, J. Zhang, D. Nie, M. Kim, Q. Wang, and D. Shen, “Deformable Image Registration Based on Similarity-Steered CNN Regression,” in Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, Quebec City, QC, Canada, 2017, pp. 300–308.
  67. L. Zhang, H. Zhang, X. Chen, Q. Wang, P.-T. Yap, and D. Shen, “Learning-Based Estimation of Functional Correlation Tensors in White Matter for Early Diagnosis of Mild Cognitive Impairment,” in Patch-Based Techniques in Medical Imaging, Quebec City, QC, Canada, 2017, pp. 65–73.
  68. J. Wang, Q. Wang, S. Wang, and D. Shen, “Sparse Multi-view Task-Centralized Learning for ASD Diagnosis,” in Machine Learning in Medical Imaging, Quebec City, QC, Canada, 2017, pp. 159–167.
  69. L. Liu, H. Zhang, I. Rekik, X. Chen, Q. Wang, and D. Shen, “Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Athens, Greece, 2016, pp. 26–34.
  70. L. Liu, Q. Wang, E. Adeli, L. Zhang, H. Zhang, and D. Shen, “Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson’s Disease,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Athens, Greece, 2016, pp. 1–8.
  71. J. Zhang, Q. Wang, G. Wu, and D. Shen, “Cross-Manifold Guidance in Deformable Registration of Brain MR Images,” in Medical Imaging and Augmented Reality, Athens, Greece, 2016, pp. 415–424.
  72. S. Wang, H. Hu, S. Su, L. Liu, Z. Wang, Q. Wang, and D. Shen, “Combination of Grey Matter and White Matter Features for Early Prediction of Posttraumatic Stress Disorder,” in Intelligent Data Engineering and Automated Learning – IDEAL 2016, Athens, Greece, 2016, pp. 560–567.
  73. P. Dong, Y. Guo, Y. Gao, P. Liang, Y. Shi, Q. Wang, D. Shen, and G. Wu, “Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning,” in Patch-Based Techniques in Medical Imaging, Athens, Greece, 2016, pp. 51–59.
  74. J. Ma, Q. Wang, Y. Ren, H. Hu, and J. Zhao, “Automatic lung nodule classification with radiomics approach,” in Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, San Diego, California, United States, 2016, vol. 9789, p. 978906.
  75. L. Zhang, Q. Wang, Y. Gao, G. Wu, and D. Shen, “Automatic Hippocampus Labeling Using the Hierarchy of Sub-region Random Forests,” in Patch-Based Techniques in Medical Imaging, Munich, Germany, 2015, pp. 19–27.
  76. G. Wu, X. Zhu, Q. Wang, and D. Shen, “Image Super-Resolution by Supervised Adaption of Patchwise Self-similarity from High-Resolution Image,” in Patch-Based Techniques in Medical Imaging, Munich, Germany, 2015, pp. 10–18.
  77. Q. Wang, G. Wu, and D. Shen, “Dual-Layer L1-Graph Embedding for Semi-supervised Image Labeling,” in Patch-Based Techniques in Medical Imaging, Munich, Germany, 2015, pp. 46–53.
  78. Q. Liu, Q. Wang, L. Zhang, Y. Gao, and D. Shen, “Multi-atlas Context Forests for Knee MR Image Segmentation,” in Machine Learning in Medical Imaging, Munich, Germany, 2015, pp. 186–193.
  79. Q. Wang, G. Wu, M.-J. Kim, L. Zhang, and D. Shen, “Interactive Registration and Segmentation for Multi-Atlas-Based Labeling of Brain MR Image,” in CCF Chinese Conference on Computer Vision, Xi’an, China, 2015, pp. 240–248.
  80. Q. Wang, G. Wu, L. Wang, P. Shi, W. Lin, and D. Shen, “Sparsity-Learning-Based Longitudinal MR Image Registration for Early Brain Development,” in Machine Learning in Medical Imaging, Boston, MA, USA, 2014, pp. 1–8.
  81. L. Zhang, Q. Wang, Y. Gao, G. Wu, and D. Shen, “Learning of Atlas Forest Hierarchy for Automatic Labeling of MR Brain Images,” in Machine Learning in Medical Imaging, Boston, MA, USA, 2014, pp. 323–330.
  82. G. Wu, Q. Wang, S. Liao, D. Zhang, F. Nie, and D. Shen, “Minimizing Joint Risk of Mislabeling for Iterative Patch-Based Label Fusion,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Nagoya, Japan, 2013, pp. 551–558.
  83. G. Wu, M. Kim, Q. Wang, Y. Gao, S. Liao, and D. Shen, “Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Nagoya, Japan, 2013, pp. 649–656.
  84. Q. Wang, L. Lu, D. Wu, N. El-Zehiry, D. Shen, and K. S. Zhou, “Automatic and Reliable Segmentation of Spinal Canals in Low-Resolution, Low-Contrast CT Images,” in Computational Methods and Clinical Applications for Spine Imaging, Nagoya, Japan, 2013, pp. 15–24.
  85. M. Kim, G. Wu, Q. Wang, and D. Shen, “Brain-Cloud: A Generalized and Flexible Registration Framework for Brain MR Images,” in Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, Nagoya, Japan, 2013, pp. 153–161.
  86. Q. Wang, M. Kim, G. Wu, and D. Shen, “Joint Learning of Appearance and Transformation for Predicting Brain MR Image Registration,” in Information Processing in Medical Imaging (IPMI), Asilomar, CA, USA, 2013, pp. 499–510.
  87. S. Ying, G. Wu, Q. Wang, and D. Shen, “Groupwise Registration via Graph Shrinkage on the Image Manifold,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, USA, 2013, pp. 2323–2330.
  88. G. Wu, M. Kim, Q. Wang, and D. Shen, “Hierarchical Attribute-Guided Symmetric Diffeomorphic Registration for MR Brain Images,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, Nice, France, 2012, pp. 90–97.
  89. Q. Lin, Z. Liang, H. Li, S. Jambawalikar, Q. Wang, B. Phillips, W. Waltzer, M. Zawin, D. Harrington, and X. He, “An image processing approach to compensate for the bladder wall motion and deformation in MR cystography,” in IEEE Nuclear Science Symposium Conference Record, 2011, pp. 3061–3065.
  90. G. Wu, Q. Wang, J. Lian, and D. Shen, “Estimating the 4D Respiratory Lung Motion by Spatiotemporal Registration and Building Super-Resolution Image,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Toronto, ON, Canada, 2011, pp. 532–539.
  91. Q. Wang, P.-T. Yap, G. Wu, and D. Shen, “Fiber Modeling and Clustering Based on Neuroanatomical Features,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Toronto, ON, Canada, 2011, pp. 17–24.
  92. Q. Wang, P.-T. Yap, G. Wu, and D. Shen, “Diffusion Tensor Image Registration with Combined Tract and Tensor Features,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Toronto, ON, Canada, 2011, pp. 200–208.
  93. H. Jia, G. Wu, Q. Wang, Y. Wang, M. Kim, and D. Shen, “Directed Graph Based Image Registration,” in Machine Learning in Medical Imaging, Toronto, ON, Canada, 2011, pp. 175–183.
  94. G. Wu, Q. Wang, J. Lian, and D. Shen, “Reconstruction of 4D-CT from a Single Free-Breathing 3D-CT by Spatial-Temporal Image Registration,” in Information Processing in Medical Imaging (IPMI), Kloster Irsee, Germany, 2011, pp. 686–698.
  95. H. Jia, G. Wu, Q. Wang, M. Kim, and D. Shen, “iTree: Fast and accurate image registration based on the combinative and incremental tree,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Chicago, Illinois, USA, 2011, pp. 1243–1246.
  96. G. Wu, Q. Wang, H. Jia, and D. Shen, “Registration of Longitudinal Image Sequences with Implicit Template and Spatial-Temporal Heuristics,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Beijing, China, 2010, pp. 618–625.
  97. G. Wu, Q. Wang, H. Jia, and D. Shen, “Groupwise Registration by Hierarchical Anatomical Correspondence Detection,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Beijing, China, 2010, pp. 684–691.
  98. G. Wu, H. Jia, Q. Wang, and D. Shen, “Groupwise Registration with Sharp Mean,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Beijing, China, 2010, pp. 570–577.
  99. Q. Wang, P.-T. Yap, H. Jia, G. Wu, and D. Shen, “Hierarchical Fiber Clustering Based on Multi-Scale Neuroanatomical Features,” in Medical Imaging and Augmented Reality, Beijing, China, 2010, pp. 448–456.
  100. H. Jia, G. Wu, Q. Wang, and D. Shen, “ABSORB: Atlas building by Self-Organized Registration and Bundling,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, California, USA, 2010, pp. 2785–2790.
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