Journal Paper

  1. Y. Guo, B. Lei, D. Wei, L. Chen, Z. Zhu, D. Feng, R. Zhang, Q. Wang, and J. Kim, “Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images,” IEEE Transactions on Cybernetics, in press.
  2. L. Si, J. Zhong, J. Huo, K. Xuan, Z. Zhuang, Y. Hu, Q. Wang, H. Zhang, and W. Yao, “Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM),” European Radiology, in press.
  3. J. Wang, F. Zhang, X. Jia, H. Zhang, S. Ying, Q. Wang, J. Shi, and D. Shen, “Multi-class ASD classification via label distribution learning with class-shared and class-specific decomposition,” Medical Image Analysis, in press.
  4. K. Xuan, L. Si, L. Zhang, Z. Xue, Y. Jiao, W. Yao, D. Shen, D. Wu, and Q. Wang, “Reducing magnetic resonance image spacing by learning without ground-truth,” Pattern Recognition, vol. 120, p. 108103, Dec. 2021.
  5. B. Xiao, N. He, Q. Wang, F. Shi, Z. Cheng, E. M. Haacke, F. Yan, and D. Shen, “Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging,” Frontiers in Neuroscience, vol. 15, p. 1498, Nov. 2021.
  6. Y. Zhang, X. Ren, B. A. Clifford, Q. Wang, and X. Zhang, “Image fusion network for dual-modal restoration,” Inverse Problems & Imaging, in press.
  7. X. Ouyang, S. Karanam, Z. Wu, T. Chen, J. Huo, X. S. Zhou, Q. Wang, and J.-Z. Cheng, “Learning hierarchical attention for weakly-supervised chest X-Ray abnormality localization and diagnosis,” IEEE Transactions on Medical Imaging, vol. 40, no. 10, pp. 2698–2710, Oct. 2021.
  8. Y. Liu, B. Xiao, C. Zhang, J. Li, Y. Lai, F. Shi, D. Shen, L. Wang, B. Sun, Y. Li, Z. Jin, H. Wei, E. M. Haacke, H. Zhou, Q. Wang, D. Li, N. He, and F. Yan, “Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson’s Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study,” Frontiers in Neuroscience, vol. 15, p. 1172, Sep. 2021.
  9. M. Zhang, L. Huang, J. Yang, W. Xu, H. Su, J. Cao, Q. Wang, J. Pu, and K. Qian, “Ultra-Fast Label-Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer,” Advanced Science, vol. 8, no. 18, p. 2101333, Aug. 2021.
  10. Y. Guo, L. Bi, Z. Zhu, D. D. Feng, R. Zhang, Q. Wang, and J. Kim, “Automatic left ventricular cavity segmentation via deep spatial sequential network in 4D computed tomography,” Computerized Medical Imaging and Graphics, vol. 91, p. 101952, Jul. 2021.
  11. W. Wang, Y. Jiao, L. Zhang, C. Fu, X. Zhu, Q. Wang, and Y. Gu, “Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage,” Acta Radiologica, p. 02841851211014188, May 2021.
  12. L. Chen, D. Gu, Y. Chen, Y. Shao, X. Cao, G. Liu, Y. Gao, Q. Wang, and D. Shen, “An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans,” Computerized Medical Imaging and Graphics, vol. 89, p. 101899, Apr. 2021.
  13. M. Zhou, L. Zhang, X. Du, X. Ouyang, X. Zhang, Q. Shen, D. Luo, X. Fan, and Q. Wang, “Hierarchical pathology screening for cervical abnormality,” Computerized Medical Imaging and Graphics, p. 101892, Mar. 2021.
  14. L. Si, K. Xuan, J. Zhong, J. Huo, Y. Xing, J. Geng, Y. Hu, H. Zhang, Q. Wang, and W. Yao, “Knee cartilage thickness differs alongside ages: A 3-T magnetic resonance research upon 2,481 subjects via deep learning,” Frontiers in Medicine, vol. 7, Feb. 2021.
  15. F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, K. He, Y. Shi, and D. Shen, “Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 4–15, Jan. 2021.
  16. D. Wei, S. Ahmad, J. Huo, P. Huang, P.-T. Yap, Z. Xue, J. Sun, W. Li, D. Shen, and Q. Wang, “SLIR: Synthesis, localization, inpainting, and registration for image-guided thermal ablation of liver tumors,” Medical Image Analysis, vol. 65, p. 101763, Oct. 2020.
  17. S. Wang, Q. Wang, Y. Shao, L. Qu, C. Lian, J. Lian, and D. Shen, “Iterative label denoising network: Segmenting male pelvic organs in CT from 3D bounding box annotations,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 10, pp. 2710–2720, Oct. 2020.
  18. J. Wang, L. Zhang, Q. Wang, L. Chen, J. Shi, X. Chen, Z. Li, and D. Shen, “Multi-class ASD classification based on functional connectivity and functional correlation tensor via multi-source domain adaptation and multi-view sparse representation,” IEEE Transactions on Medical Imaging, vol. 39, no. 10, pp. 3137–3147, Oct. 2020.
  19. J. Huo, Z. Qi, S. Chen, Q. Wang, X. Wu, D. Zang, T. Hiromi, J. Tan, L. Zhang, W. Tang, and D. Shen, “Neuroimage-based consciousness evaluation of patients with secondary doubtful hydrocephalus before and after lumbar drainage,” Neuroscience Bulletin, vol. 36, no. 9, pp. 985–996, Sep. 2020.
  20. X. Ouyang, J. Huo, L. Xia, F. Shan, J. Liu, Z. Mo, F. Yan, Z. Ding, Q. Yang, B. Song, F. Shi, H. Yuan, Y. Wei, X. Cao, Y. Gao, D. Wu, Q. Wang, and D. Shen, “Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2595–2605, Aug. 2020.
  21. L. Huang, L. Wang, X. Hu, S. Chen, Y. Tao, H. Su, J. Yang, W. Xu, V. Vedarethinam, S. Wu, B. Liu, X. Wan, J. Lou, Q. Wang, and K. Qian, “Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma,” Nature Communications, vol. 11, no. 1, p. 3556, Jul. 2020.
  22. S. Wang, D. Nie, L. Qu, Y. Shao, J. Lian, Q. Wang, and D. Shen, “CT male pelvic organ segmentation via hybrid loss network with incomplete annotation,” IEEE Transactions on Medical Imaging, vol. 39, no. 6, pp. 2151–2162, Jun. 2020.
  23. X. Ren, S. Ahmad, L. Zhang, L. Xiang, D. Nie, F. Yang, Q. Wang, and D. Shen, “Task decomposition and synchronization for semantic biomedical image segmentation,” IEEE Transactions on Image Processing, vol. 29, pp. 7497–7510, Jun. 2020.
  24. D. Wei, L. Zhang, Z. Wu, X. Cao, G. Li, D. Shen, and Q. Wang, “Deep morphological simplification network (MS-Net) for guided registration of brain magnetic resonance images,” Pattern Recognition, vol. 100, p. 107171, Apr. 2020.
  25. J. Cao, X. Shi, D. D. Gurav, L. Huang, H. Su, K. Li, J. Niu, M. Zhang, Q. Wang, M. Jiang, and K. Qian, “Metabolic Fingerprinting on Synthetic Alloys for Medulloblastoma Diagnosis and Radiotherapy Evaluation,” Advanced Materials, vol. 32, no. 23, p. 2000906, Apr. 2020.
  26. T. Kurc, S. Bakas, X. Ren, A. Bagari, A. Momeni, Y. Huang, L. Zhang, A. Kumar, M. Thibault, Q. Qi, Q. Wang, A. Kori, O. Gevaert, Y. Zhang, D. Shen, M. Khened, X. Ding, G. Krishnamurthi, J. Kalpathy-Cramer, J. Davis, T. Zhao, R. Gupta, J. Saltz, and K. Farahani, “Segmentation and classification in digital pathology for glioma research: challenges and deep learning approaches,” Frontiers in Neuroscience, vol. 14, Feb. 2020.
  27. J. Yang, R. Wang, L. Huang, M. Zhang, J. Niu, C. Bao, N. Shen, M. Dai, Q. Guo, Q. Wang, Q. Wang, Q. Fu, and K. Qian, “Urine metabolic fingerprints encode subtypes of kidney diseases,” Angewandte Chemie, vol. 132, no. 4, pp. 1720–1727, Jan. 2020.
  28. J. Fan, X. Cao, Q. Wang, P.-T. Yap, and D. Shen, “Adversarial learning for mono- or multi-modal registration,” Medical Image Analysis, vol. 58, p. 101545, Dec. 2019.
  29. B. Xiao, N. He, Q. Wang, Z. Cheng, Y. Jiao, E. M. Haacke, F. Yan, and F. Shi, “Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson’s disease,” NeuroImage: Clinical, vol. 24, p. 102070, Nov. 2019.
  30. S. Wu, Y. Jiao, Y. Zhang, X. Ren, P. Li, Q. Yu, Q. Zhang, Q. Wang, and S. Fu, “Imaging-based individualized response prediction of carbon ion radiotherapy for prostate cancer patients,” Cancer Management and Research, vol. 11, pp. 9121–9131, Oct. 2019.
  31. L. Liu, H. Zhang, J. Wu, Z. Yu, X. Chen, I. Rekik, Q. Wang, J. Lu, and D. Shen, “Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks,” Brain Imaging and Behavior, vol. 13, no. 5, pp. 1333–1351, Oct. 2019.
  32. Z. Fang, Y. Chen, M. Liu, L. Xiang, Q. Zhang, Q. Wang, W. Lin, and D. Shen, “Deep learning for fast and spatially constrained tissue quantification from highly accelerated data in magnetic resonance fingerprinting,” IEEE Transactions on Medical Imaging, vol. 38, no. 10, pp. 2364–2374, Oct. 2019.
  33. X. Ren, T. Li, X. Yang, S. Wang, S. Ahmad, L. Xiang, S. R. Stone, L. Li, Y. Zhan, D. Shen, and Q. Wang, “Regression convolutional neural network for automated pediatric bone age assessment from hand radiograph,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 5, pp. 2030–2038, Sep. 2019.
  34. J. Wang, Q. Wang, H. Zhang, J. Chen, S. Wang, and D. Shen, “Sparse multiview task-centralized ensemble learning for ASD diagnosis based on age- and sex-related functional connectivity patterns,” IEEE Transactions on Cybernetics, vol. 49, no. 8, pp. 3141–3154, Aug. 2019.
  35. H. Song, Y. Jiao, W. Wei, X. Ren, C. Shen, Z. Qiu, Q. Yang, Q. Wang, and Q.-Y. Luo, “Can pretreatment 18F-FDG PET tumor texture features predict the outcomes of osteosarcoma treated by neoadjuvant chemotherapy?,” European Radiology, vol. 29, no. 7, pp. 3945–3954, Jul. 2019.
  36. L. Xiang, Y. Chen, W. Chang, Y. Zhan, W. Lin, Q. Wang, and D. Shen, “Deep-learning-based multi-modal fusion for fast MR reconstruction,” IEEE Transactions on Biomedical Engineering, vol. 66, no. 7, pp. 2105–2114, Jul. 2019.
  37. Y. Shi and Q. Wang, “The artificial intelligence-enabled medical imaging: Today and its future,” Chinese Medical Sciences Journal, vol. 34, no. 2, pp. 71–75, Jun. 2019.
  38. Y. Shao, J. Kim, Y. Gao, Q. Wang, W. Lin, and D. Shen, “Hippocampal segmentation from longitudinal infant brain MR images via classification-guided boundary regression,” IEEE Access, vol. 7, pp. 33728–33740, Mar. 2019.
  39. D. Nie, J. Lu, H. Zhang, E. Adeli, J. Wang, Z. Yu, L. Liu, Q. Wang, J. Wu, and D. Shen, “Multi-channel 3D deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages,” Scientific Reports, vol. 9, no. 1, pp. 1–14, Jan. 2019.
  40. D. Nie, R. Trullo, J. Lian, L. Wang, C. Petitjean, S. Ruan, Q. Wang, and D. Shen, “Medical image synthesis with deep convolutional adversarial networks,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 12, pp. 2720–2730, Dec. 2018.
  41. L. Lu, S. Wang, Q. Wang, Y. Shao, X. Wei, Y. Li, and W. Li, “Diffusion kurtosis as an in vivo imaging marker of early radiation-induced changes in radiation-induced temporal lobe necrosis in nasopharyngeal carcinoma patients,” Clinical Neuroradiology, vol. 28, no. 3, pp. 413–420, Sep. 2018.
  42. C. Liu, C. Liu, L. Si, H. Shen, Q. Wang, and W. Yao, “Relationship between subchondral bone microstructure and articular cartilage in the osteoarthritic knee using 3T MRI,” Journal of Magnetic Resonance Imaging, vol. 48, no. 3, pp. 669–679, Sep. 2018.
  43. X. Cao, J. Yang, J. Zhang, Q. Wang, P.-T. Yap, and D. Shen, “Deformable image registration using a cue-aware deep regression network,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 9, pp. 1900–1911, Sep. 2018.
  44. L. Chen, H. Zhang, J. Lu, K. Thung, A. Aibaidula, L. Liu, S. Chen, L. Jin, J. Wu, Q. Wang, L. Zhou, and D. Shen, “Multi-label nonlinear matrix completion with transductive multi-task feature selection for joint MGMT and IDH1 status prediction of patient with high-grade gliomas,” IEEE Transactions on Medical Imaging, vol. 37, no. 8, pp. 1775–1787, Aug. 2018.
  45. L. Xiang, Q. Wang, D. Nie, L. Zhang, X. Jin, Y. Qiao, and D. Shen, “Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image,” Medical Image Analysis, vol. 47, pp. 31–44, Jul. 2018.
  46. L. Liu, Q. Wang, E. Adeli, L. Zhang, H. Zhang, and D. Shen, “Exploring diagnosis and imaging biomarkers of Parkinson’s disease via iterative canonical correlation analysis based feature selection,” Computerized Medical Imaging and Graphics, vol. 67, pp. 21–29, Jul. 2018.
  47. X. Cao, J. Yang, Y. Gao, Q. Wang, and D. Shen, “Region-adaptive deformable registration of CT/MRI pelvic images via learning-based image synthesis,” IEEE Transactions on Image Processing, vol. 27, no. 7, pp. 3500–3512, Jul. 2018.
  48. X. Ren, L. Xiang, D. Nie, Y. Shao, H. Zhang, D. Shen, and Q. Wang, “Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images,” Medical Physics, vol. 45, no. 5, pp. 2063–2075, May 2018.
  49. L.-Y. Zhang, P. Lin, J. Pan, Y. Ma, Z. Wei, J. Lu, L. Wang, Y. Song, Y. Wang, Z. Zhang, K. Jin, Q. Wang, and G.-Y. Yang, “CLARITY for high-resolution imaging and quantification of vasculature in the whole mouse brain,” Aging and disease, vol. 9, no. 2, pp. 262–272, Apr. 2018.
  50. X. Sun, L. Huang, R. Zhang, W. Xu, J. Huang, D. D. Gurav, V. Vedarethinam, R. Chen, J. Lou, Q. Wang, J. Wan, and K. Qian, “Metabolic fingerprinting on a plasmonic gold chip for mass spectrometry based in vitro diagnostics,” ACS Central Science, vol. 4, no. 2, pp. 223–229, Feb. 2018.
  51. L. Xiang, Y. Qiao, D. Nie, L. An, W. Lin, Q. Wang, and D. Shen, “Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI,” Neurocomputing, vol. 267, pp. 406–416, Dec. 2017.
  52. C. Liu, C. Liu, X. Ren, L. Si, H. Shen, Q. Wang, and W. Yao, “Quantitative evaluation of subchondral bone microarchitecture in knee osteoarthritis using 3T MRI,” BMC Musculoskeletal Disorders, vol. 18, no. 1, p. 496, Nov. 2017.
  53. L. Zhang, H. Zhang, X. Chen, Q. Wang, P.-T. Yap, and D. Shen, “Learning-based structurally-guided construction of resting-state functional correlation tensors,” Magnetic Resonance Imaging, vol. 43, pp. 110–121, Nov. 2017.
  54. J. Xiong, Y. Shao, J. Ma, Y. Ren, Q. Wang, and J. Zhao, “Lung field segmentation using weighted sparse shape composition with robust initialization,” Medical Physics, vol. 44, no. 11, pp. 5916–5929, Nov. 2017.
  55. Y. Hou, S. H. Park, Q. Wang, J. Zhang, X. Zong, W. Lin, and D. Shen, “Enhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering,” Scientific Reports, vol. 7, no. 1, pp. 1–12, Aug. 2017.
  56. J. Ma, Z. Zhou, Y. Ren, J. Xiong, L. Fu, Q. Wang, and J. Zhao, “Computerized detection of lung nodules through radiomics,” Medical Physics, vol. 44, no. 8, pp. 4148–4158, Aug. 2017.
  57. J. Wang, Q. Wang, J. Peng, D. Nie, F. Zhao, M. Kim, H. Zhang, C.-Y. Wee, S. Wang, and D. Shen, “Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study,” Human Brain Mapping, vol. 38, no. 6, pp. 3081–3097, Jun. 2017.
  58. H.-X. Li, X. Feng, Q. Wang, X. Dong, M. Yu, and W.-J. Tu, “Diffusion tensor imaging assesses white matter injury in neonates with hypoxic-ischemic encephalopathy,” Neural Regeneration Research, vol. 12, no. 4, pp. 603–609, Apr. 2017.
  59. L. Zhang, Q. Wang, Y. Gao, H. Li, G. Wu, and D. Shen, “Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images,” Neurocomputing, vol. 229, pp. 3–12, Mar. 2017.
  60. J. Zhang, L. Zhang, L. Xiang, Y. Shao, G. Wu, X. Zhou, D. Shen, and Q. Wang, “Brain atlas fusion from high-thickness diagnostic magnetic resonance images by learning-based super-resolution,” Pattern Recognition, vol. 63, pp. 531–541, Mar. 2017.
  61. Q. Wang and D. Shen, “Computational medicine: A cybernetic eye for rare disease,” Nature Biomedical Engineering, vol. 1, no. 2, pp. 1–2, Feb. 2017.
  62. G. Wu, M. Kim, Q. Wang, B. C. Munsell, and D. Shen, “Scalable high-performance image registration framework by unsupervised deep feature representations learning,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1505–1516, Jul. 2016.
  63. S. Peng, S. Yu, Q. Wang, Q. Kang, Y. Zhang, R. Zhang, W. Jiang, Y. Qian, H. Zhang, M. Zhang, Z. Xiao, and J. Chen, “Dopamine receptor D2 and catechol-O-methyltransferase gene polymorphisms associated with anorexia nervosa in Chinese Han population: DRD2 and COMT gene polymorphisms were associated with AN,” Neuroscience Letters, vol. 616, pp. 147–151, Mar. 2016.
  64. L. Zhang, Q. Wang, Y. Gao, G. Wu, and D. Shen, “Automatic labeling of MR brain images by hierarchical learning of atlas forests,” Medical Physics, vol. 43, no. 3, pp. 1175–1186, Mar. 2016.
  65. G. Wu, X. Peng, S. Ying, Q. Wang, P.-T. Yap, D. Shen, and D. Shen, “eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration,” PLOS ONE, vol. 11, no. 1, p. e0146870, Jan. 2016.
  66. H. Hu, Y. Zhou, Q. Wang, S. Su, Y. Qiu, J. Ge, Z. Wang, and Z. Xiao, “Association of abnormal white matter integrity in the acute phase of motor vehicle accidents with post-traumatic stress disorder,” Journal of Affective Disorders, vol. 190, pp. 714–722, Jan. 2016.
  67. Y. Shao, Y. Gao, Q. Wang, X. Yang, and D. Shen, “Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images,” Medical Image Analysis, vol. 26, no. 1, pp. 345–356, Dec. 2015.
  68. Q. Wang, L. Lu, D. Wu, N. El-Zehiry, Y. Zheng, D. Shen, and K. S. 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, Aug. 2015.
  69. Y. Wu, G. Wu, L. Wang, B. C. Munsell, Q. Wang, W. Lin, Q. Feng, W. Chen, and D. Shen, “Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection,” Medical Physics, vol. 42, no. 7, pp. 4174–4189, Jul. 2015.
  70. G. Wu, M. Kim, G. Sanroma, Q. Wang, B. C. Munsell, and D. Shen, “Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition,” NeuroImage, vol. 106, pp. 34–46, Feb. 2015.
  71. Q. Wang, M. Kim, Y. Shi, G. Wu, and D. Shen, “Predict brain MR image registration via sparse learning of appearance and transformation,” Medical Image Analysis, vol. 20, no. 1, pp. 61–75, Feb. 2015.
  72. M. Kim, G. Wu, Q. Wang, S.-W. Lee, and D. Shen, “Improved image registration by sparse patch-based deformation estimation,” NeuroImage, vol. 105, pp. 257–268, Jan. 2015.
  73. Q. Liu and Q. Wang, “Groupwise registration of brain magnetic resonance images: A review,” Journal of Shanghai Jiaotong University (Science), vol. 19, no. 6, pp. 755–762, Dec. 2014.
  74. R. Min, G. Wu, J. Cheng, Q. Wang, and D. Shen, “Multi-atlas based representations for Alzheimer’s disease diagnosis,” Human Brain Mapping, vol. 35, no. 10, pp. 5052–5070, Oct. 2014.
  75. G. Wu, Q. Wang, D. Zhang, F. Nie, H. Huang, and D. Shen, “A generative probability model of joint label fusion for multi-atlas based brain segmentation,” Medical Image Analysis, vol. 18, no. 6, pp. 881–890, Aug. 2014.
  76. Q. Wang, P.-T. Yap, G. Wu, and D. Shen, “Diffusion tensor image registration using hybrid connectivity and tensor features,” Human Brain Mapping, vol. 35, no. 7, pp. 3529–3546, Jul. 2014.
  77. G. Wu, M. Kim, Q. Wang, and D. Shen, “S-HAMMER: Hierarchical attribute-guided, symmetric diffeomorphic registration for MR brain images,” Human Brain Mapping, vol. 35, no. 3, pp. 1044–1060, Mar. 2014.
  78. B. Jie, D. Zhang, W. Gao, Q. Wang, C.-Y. Wee, and D. Shen, “Integration of network topological and connectivity properties for neuroimaging classification,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 2, pp. 576–589, Feb. 2014.
  79. S. Ying, G. Wu, Q. Wang, and D. Shen, “Hierarchical unbiased graph shrinkage (HUGS): A novel groupwise registration for large data set,” NeuroImage, vol. 84, pp. 626–638, Jan. 2014.
  80. Q. Wang, P.-T. Yap, G. Wu, and D. Shen, “Application of neuroanatomical features to tractography clustering,” Human Brain Mapping, vol. 34, no. 9, pp. 2089–2102, Sep. 2013.
  81. G. Wu, Q. Wang, J. Lian, and D. Shen, “Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction,” Medical Physics, vol. 40, no. 3, p. 031710, Mar. 2013.
  82. H. Jia, G. Wu, Q. Wang, Y. Wang, M. Kim, and D. Shen, “Directed graph based image registration,” Computerized Medical Imaging and Graphics, vol. 36, no. 2, pp. 139–151, Mar. 2012.
  83. G. Wu, Q. Wang, H. Jia, and D. Shen, “Feature-based groupwise registration by hierarchical anatomical correspondence detection,” Human Brain Mapping, vol. 33, no. 2, pp. 253–271, Feb. 2012.
  84. G. Wu, Q. Wang, and D. Shen, “Registration of longitudinal brain image sequences with implicit template and spatial–temporal heuristics,” NeuroImage, vol. 59, no. 1, pp. 404–421, Jan. 2012.
  85. G. Wu, H. Jia, Q. Wang, and D. Shen, “SharpMean: Groupwise registration guided by sharp mean image and tree-based registration,” NeuroImage, vol. 56, no. 4, pp. 1968–1981, Jun. 2011.
  86. H. Jia, P.-T. Yap, G. Wu, Q. Wang, and D. Shen, “Intermediate templates guided groupwise registration of diffusion tensor images,” NeuroImage, vol. 54, no. 2, pp. 928–939, Jan. 2011.
  87. Q. Wang, L. Chen, P.-T. Yap, G. Wu, and D. Shen, “Groupwise registration based on hierarchical image clustering and atlas synthesis,” Human Brain Mapping, vol. 31, no. 8, pp. 1128–1140, Aug. 2010.
  88. H. Jia, G. Wu, Q. Wang, and D. Shen, “ABSORB: Atlas building by self-organized registration and bundling,” NeuroImage, vol. 51, no. 3, pp. 1057–1070, Jul. 2010.
  89. Q. Wang, G. Wu, P.-T. Yap, and D. Shen, “Attribute vector guided groupwise registration,” NeuroImage, vol. 50, no. 4, pp. 1485–1496, May 2010.