Dinggang Shen


Affiliation: University of North Carolina
Country: USA


  1. Shen D, Wu G, Suk H. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng. 2017;19:221-248 pubmed publisher
    ..We conclude by discussing research issues and suggesting future directions for further improvement. ..
  2. Li Y, Liu J, Peng Z, Sheng C, Kim M, Yap P, et al. Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification. Neuroinformatics. 2019;: pubmed publisher
    ..We finally fuse the low-order and the high-order networks using two decision trees for MCI classification. Experimental results demonstrate the effectiveness of the proposed method on MCI classification. ..
  3. Wang S, He K, Nie D, Zhou S, Gao Y, Shen D. CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation. Med Image Anal. 2019;54:168-178 pubmed publisher
    ..Experimental results show that the performance of our proposed method outperforms the baseline fully convolutional networks, as well as other state-of-the-art methods in CT male pelvic organ segmentation. ..
  4. Fan J, Cao X, Yap P, Shen D. BIRNet: Brain image registration using dual-supervised fully convolutional networks. Med Image Anal. 2019;54:193-206 pubmed publisher
    ..Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods. ..
  5. Chen Y, Shi M, Gao H, Shen D, Cai L, Ji S. Voxel Deconvolutional Networks for 3D Brain Image Labeling. KDD. 2018;2018:1226-1234 pubmed publisher
    ..39% and 2.21% respectively compared with the baseline. In addition, all the variations of VoxelDCN we proposed outperform the baseline methods on the above datasets, which demonstrates the effectiveness of our methods. ..
  6. Wang M, Zhang D, Shen D, Liu M. Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data. Med Image Anal. 2019;53:111-122 pubmed publisher
    ..In comparison with several state-of-the-art methods, our proposed method can achieve promising performance for cognitive status prediction and also can help discover disease-related biomarkers. ..
  7. Zhang H, Giannakopoulos P, Haller S, Lee S, Qiu S, Shen D. Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment. Neuroinformatics. 2019;: pubmed publisher
    ..This work demonstrates the biological meaning and potential diagnostic value of the IN-HOFC in clinical neuroscience studies. ..
  8. Nie D, Lu J, Zhang H, Adeli E, Wang J, Yu Z, et al. Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages. Sci Rep. 2019;9:1103 pubmed publisher
    ..This study indicates highly demanded effectiveness on prognosis of deep learning technique in neuro-oncological applications for better individualized treatment planning towards precision medicine. ..
  9. Chen G, Wu Y, Shen D, Yap P. Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space. Med Image Anal. 2019;53:79-94 pubmed publisher
    ..Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively. ..

More Information


  1. Li Y, Liu J, Gao X, Jie B, Kim M, Yap P, et al. Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification. Med Image Anal. 2019;52:80-96 pubmed publisher
    ..Experimental results on publicly available ADNI dataset demonstrate that the proposed method outperforms the existing single modality based sparse functional connectivity inference methods. ..
  2. Fang L, Zhang L, Nie D, Cao X, Rekik I, Lee S, et al. Automatic brain labeling via multi-atlas guided fully convolutional networks. Med Image Anal. 2019;51:157-168 pubmed publisher
    ..Experimental results on different datasets demonstrate the effectiveness of our proposed method, by significantly outperforming the conventional FCN and several state-of-the-art MR brain labeling methods. ..
  3. Wen X, Zhang H, Li G, Liu M, Yin W, Lin W, et al. First-year development of modules and hubs in infant brain functional networks. Neuroimage. 2019;185:222-235 pubmed publisher
  4. Liu L, Zhang H, Wu J, Yu Z, Chen X, Rekik I, et al. Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks. Brain Imaging Behav. 2018;: pubmed publisher
    ..g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning. ..
  5. Kamiya N, Li J, Kume M, Fujita H, Shen D, Zheng G. Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications. Int J Comput Assist Radiol Surg. 2018;13:1697-1706 pubmed publisher
    ..Our fully automatic, learning-based method can accurately segment paraspinal muscles from 3D torso CT images. It generates segmentation results that are better than those achieved by the state-of-the-art methods. ..
  6. Xia J, Wang F, Meng Y, Wu Z, Wang L, Lin W, et al. A computational method for longitudinal mapping of orientation-specific expansion of cortical surface in infants. Med Image Anal. 2018;49:46-59 pubmed publisher
    ..We have applied the proposed method to 30 healthy infants, and for the first time we revealed the orientation-specific longitudinal cortical surface expansion maps during the first postnatal year. ..
  7. Duan D, Xia S, Rekik I, Meng Y, Wu Z, Wang L, et al. Exploring folding patterns of infant cerebral cortex based on multi-view curvature features: Methods and applications. Neuroimage. 2019;185:575-592 pubmed publisher
    ..Moreover, we have also validated the proposed method on a public adult dataset, i.e., the Human Connectome Project (HCP), and revealed that certain major cortical folding patterns of adults are largely established at term birth. ..
  8. Lian C, Zhang J, Liu M, Zong X, Hung S, Lin W, et al. Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images. Med Image Anal. 2018;46:106-117 pubmed publisher
    ..The proposed multi-channel multi-scale FCN has been evaluated on the 7T brain MR images scanned from 20 subjects. The experimental results show its superior performance compared with several state-of-the-art methods. ..
  9. Xiang L, Wang Q, Nie D, Zhang L, Jin X, Qiao Y, et al. Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image. Med Image Anal. 2018;47:31-44 pubmed publisher
  10. Shao Y, Gao Y, Wang Q, Yang X, Shen D. Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images. Med Image Anal. 2015;26:345-56 pubmed publisher
    ..Compared with other state-of-the-art methods, our method also shows a competitive performance. ..
  11. Li G, Wang L, Shi F, Lyall A, Ahn M, Peng Z, et al. Cortical thickness and surface area in neonates at high risk for schizophrenia. Brain Struct Funct. 2016;221:447-61 pubmed publisher
    ..This preliminary study provides the first evidence that early development of cortical thickness and surface area might be abnormal in the neonates at genetic risk for schizophrenia. ..
  12. Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage. 2015;108:214-24 pubmed publisher
    ..In addition, our results indicated that integration of multi-modality images led to significant performance improvement. ..
  13. Li G, Wang L, Shi F, Gilmore J, Lin W, Shen D. Construction of 4D high-definition cortical surface atlases of infants: Methods and applications. Med Image Anal. 2015;25:22-36 pubmed publisher
  14. Liu L, Wang Q, Adeli E, Zhang L, Zhang H, Shen D. Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection. Comput Med Imaging Graph. 2018;67:21-29 pubmed publisher
    ..We also demonstrate that using the proposed feature selection method, the PD diagnosis performance is further improved, and also outperforms many state-of-the-art methods. ..
  15. Kim M, Wu G, Wang Q, Lee S, Shen D. Improved image registration by sparse patch-based deformation estimation. Neuroimage. 2015;105:257-68 pubmed publisher
    ..Experimental results on both simulated and real data show that the registration performance can be significantly improved after integrating our patch-based deformation prediction framework into the existing registration algorithms. ..
  16. Adeli E, Meng Y, Li G, Lin W, Shen D. Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data. Neuroimage. 2019;185:783-792 pubmed publisher
    ..e., root mean square error of 0.18). ..
  17. Wang Q, Kim M, Shi Y, Wu G, Shen D. Predict brain MR image registration via sparse learning of appearance and transformation. Med Image Anal. 2015;20:61-75 pubmed publisher
    ..We apply our method to registering brain MR images, and conclude that the proposed framework is competent to improve registration performances substantially. ..
  18. Wang L, Gao Y, Shi F, Li G, Gilmore J, Lin W, et al. LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images. Neuroimage. 2015;108:160-72 pubmed publisher
    ..Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy. ..
  19. Cheng B, Liu M, Suk H, Shen D, Zhang D. Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging Behav. 2015;9:913-26 pubmed publisher
    ..1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods. ..
  20. Han D, Gao Y, Wu G, Yap P, Shen D. Robust anatomical landmark detection with application to MR brain image registration. Comput Med Imaging Graph. 2015;46 Pt 3:277-90 pubmed publisher
    ..Experimental results show that our method is capable of providing good initialization even for the images with large deformation difference, thus improving registration accuracy. ..
  21. Yin Q, Hung S, Rathmell W, Shen L, Wang L, Lin W, et al. Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma. Clin Radiol. 2018;73:782-791 pubmed publisher
    ..Featured with high accuracy, an integrated multi-omics model of PET/MRI-based radiomics could be the first non-invasive investigation for disease risk stratification and guidance of treatment in patients with primary ccRCC. ..
  22. Wu G, Kim M, Sanroma G, Wang Q, Munsell B, Shen D. Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. Neuroimage. 2015;106:34-46 pubmed publisher
  23. Wang L, Wee C, Tang X, Yap P, Shen D. Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder. Brain Imaging Behav. 2016;10:33-40 pubmed publisher
    ..Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals. ..
  24. Zhang H, Shen D, Lin W. Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts. Neuroimage. 2019;185:664-684 pubmed publisher
    ..In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics. ..
  25. Zhu X, Suk H, Lee S, Shen D. Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis. Brain Imaging Behav. 2016;10:818-28 pubmed publisher
    ..The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods. ..
  26. Cheng B, Liu M, Zhang D, Shen D. Robust multi-label transfer feature learning for early diagnosis of Alzheimer's disease. Brain Imaging Behav. 2019;13:138-153 pubmed publisher
    ..The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods. ..
  27. Wee C, Yang S, Yap P, Shen D. Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging Behav. 2016;10:342-56 pubmed publisher
  28. Li G, Wang L, Yap P, Wang F, Wu Z, Meng Y, et al. Computational neuroanatomy of baby brains: A review. Neuroimage. 2019;185:906-925 pubmed publisher
    ..Finally, we discuss the limitations in current research and suggest potential future research directions. ..
  29. Li G, Lin W, Gilmore J, Shen D. Spatial Patterns, Longitudinal Development, and Hemispheric Asymmetries of Cortical Thickness in Infants from Birth to 2 Years of Age. J Neurosci. 2015;35:9150-62 pubmed publisher
    ..This study provides the first comprehensive picture of early patterns and evolution of CT during infancy. ..
  30. Jie B, Liu M, Shen D. Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease. Med Image Anal. 2018;47:81-94 pubmed publisher
  31. Meng Y, Li G, Gao Y, Lin W, Shen D. Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies. Hum Brain Mapp. 2016;37:4129-4147 pubmed publisher
    ..23 mm. Hum Brain Mapp 37:4129-4147, 2016. © 2016 Wiley Periodicals, Inc. ..
  32. Zong X, Park S, Shen D, Lin W. Visualization of perivascular spaces in the human brain at 7T: sequence optimization and morphology characterization. Neuroimage. 2016;125:895-902 pubmed publisher
  33. Rekik I, Li G, Lin W, Shen D. Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Med Image Anal. 2016;28:1-12 pubmed publisher
    ..Our method attained a higher prediction accuracy and better captured the spatiotemporal dynamic change of the highly folded cortical surface than the previous proposed prediction method. ..
  34. Adeli E, Shi F, An L, Wee C, Wu G, Wang T, et al. Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data. Neuroimage. 2016;141:206-219 pubmed publisher
    ..The obtained results indicate that our proposed method can identify the imaging biomarkers and diagnose the disease with favorable accuracies compared to the baseline methods. ..
  35. Qian C, Wang L, Gao Y, Yousuf A, Yang X, Oto A, et al. In vivo MRI based prostate cancer localization with random forests and auto-context model. Comput Med Imaging Graph. 2016;52:44-57 pubmed publisher
    ..The common ROC analysis with the AUC value of 0.832 and also the ROI-based ROC analysis with the AUC value of 0.883 both illustrate the effectiveness of our proposed method. ..
  36. Qiao L, Zhang H, Kim M, Teng S, Zhang L, Shen D. Estimating functional brain networks by incorporating a modularity prior. Neuroimage. 2016;141:399-407 pubmed publisher
    ..Moreover, we further explore brain network features that contributed to MCI identification, and discovered potential biomarkers for personalized diagnosis. ..
  37. Suk H, Lee S, Shen D. Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis. Brain Struct Funct. 2016;221:2569-87 pubmed publisher
    ..In our experiments on the ADNI cohort, we performed both binary and multi-class classification tasks in AD/MCI diagnosis and showed the superiority of the proposed method by comparing with the state-of-the-art methods. ..
  38. Zhao F, Qiao L, Shi F, Yap P, Shen D. Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder. Brain Imaging Behav. 2017;11:1050-1060 pubmed publisher
    ..These brain regions include the putamen, precuneus, and orbitofrontal cortex, which are highly associated with human emotional modulation and memory formation. These finding are consistent with the behavioral phenotype of ASD. ..
  39. Jiang W, Shi F, Liao J, Liu H, Wang T, Shen C, et al. Disrupted functional connectome in antisocial personality disorder. Brain Imaging Behav. 2017;11:1071-1084 pubmed publisher
    ..These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD. ..
  40. Jiang W, Li G, Liu H, Shi F, Wang T, Shen C, et al. Reduced cortical thickness and increased surface area in antisocial personality disorder. Neuroscience. 2016;337:143-152 pubmed publisher
    ..These cortical structural changes may introduce uncontrolled and callous behavioral characteristic in ASPD patients, and these potential biomarkers may be very helpful in understanding the pathomechanism of ASPD. ..
  41. Yu G, Liu Y, Shen D. Graph-guided joint prediction of class label and clinical scores for the Alzheimer's disease. Brain Struct Funct. 2016;221:3787-801 pubmed publisher
    ..To validate our method, we have performed many numerical studies using simulated datasets and the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with the other methods, our proposed method has very promising performance. ..
  42. Du S, Guo Y, Sanroma G, Ni D, Wu G, Shen D. Building dynamic population graph for accurate correspondence detection. Med Image Anal. 2015;26:256-67 pubmed publisher
  43. Thung K, Wee C, Yap P, Shen D. Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans. Brain Struct Funct. 2016;221:3979-3995 pubmed
    ..6 % higher than the case using only the reference time point image. In other words, information provided by the longitudinal history of the disease improves diagnosis accuracy. ..
  44. Cheng B, Liu M, Shen D, Li Z, Zhang D. Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease. Neuroinformatics. 2017;15:115-132 pubmed publisher
    ..The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods. ..
  45. Rekik I, Li G, Lin W, Shen D. Multidirectional and Topography-based Dynamic-scale Varifold Representations with Application to Matching Developing Cortical Surfaces. Neuroimage. 2016;135:152-62 pubmed publisher
  46. Thung K, Yap P, Adeli E, Lee S, Shen D. Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion. Med Image Anal. 2018;45:68-82 pubmed publisher
    ..Evaluations using the ADNI dataset indicate that our method outperforms conventional LRMC and other state-of-the-art methods. Our method achieves a maximal pMCI classification accuracy of 84% and time prediction correlation of 0.665. ..
  47. Zhang P, Wu G, Gao Y, Yap P, Shen D. A dynamic tree-based registration could handle possible large deformations among MR brain images. Comput Med Imaging Graph. 2016;52:1-7 pubmed publisher
    ..Evaluation based on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy. ..
  48. Jie B, Wee C, Shen D, Zhang D. Hyper-connectivity of functional networks for brain disease diagnosis. Med Image Anal. 2016;32:84-100 pubmed publisher
  49. Huang L, Jin Y, Gao Y, Thung K, Shen D. Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest. Neurobiol Aging. 2016;46:180-91 pubmed publisher
    ..Our method can also be extended to be a general regression framework to predict other disease scores. ..
  50. Sanroma G, Wu G, Gao Y, Thung K, Guo Y, Shen D. A transversal approach for patch-based label fusion via matrix completion. Med Image Anal. 2015;24:135-48 pubmed publisher
    ..We achieve more accurate segmentation results than both reconstruction-based and classification-based approaches. Our label fusion method is also ranked 1st in the online SATA Multi-Atlas Segmentation Challenge. ..
  51. Huang H, Lu J, Wu J, Ding Z, Chen S, Duan L, et al. Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis. Sci Rep. 2018;8:1223 pubmed publisher
    ..Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment. ..
  52. Zu C, Jie B, Liu M, Chen S, Shen D, Zhang D. Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment. Brain Imaging Behav. 2016;10:1148-1159 pubmed
    ..The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI. ..
  53. Zhang L, Zhang H, Chen X, Wang Q, Yap P, Shen D. Learning-based structurally-guided construction of resting-state functional correlation tensors. Magn Reson Imaging. 2017;43:110-121 pubmed publisher
    ..We have demonstrated the utility of our enhanced FCTs in Alzheimer's disease (AD) diagnosis by identifying mild cognitive impairment (MCI) patients from normal subjects. ..
  54. Zhang Y, Zhang H, Chen X, Lee S, Shen D. Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis. Sci Rep. 2017;7:6530 pubmed publisher
    ..Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome. ..
  55. Wu Z, Gao Y, Shi F, Ma G, Jewells V, Shen D. Segmenting hippocampal subfields from 3T MRI with multi-modality images. Med Image Anal. 2018;43:10-22 pubmed publisher
  56. Lu J, Zhang H, Hameed N, Zhang J, Yuan S, Qiu T, et al. An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning. Sci Rep. 2017;7:13769 pubmed publisher
    ..8%]) after extending to a radius of 1?cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients. ..
  57. Liu M, Zhang J, Adeli E, Shen D. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal. 2018;43:157-168 pubmed publisher
    ..e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. ..
  58. Wu Z, Guo Y, Park S, Gao Y, Dong P, Lee S, et al. Robust brain ROI segmentation by deformation regression and deformable shape model. Med Image Anal. 2018;43:198-213 pubmed publisher
    ..The results consistently show that our method is better in terms of both segmentation accuracy and computational efficiency. ..
  59. Song Y, Wu G, Bahrami K, Sun Q, Shen D. Progressive multi-atlas label fusion by dictionary evolution. Med Image Anal. 2017;36:162-171 pubmed publisher
  60. Rekik I, Li G, Yap P, Chen G, Lin W, Shen D. Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI. Neuroimage. 2017;152:411-424 pubmed publisher
    ..Ultimately, devising accurate shape evolution prediction models that can help quantify and predict the severity of a brain disorder as it progresses will be of great aid in individualized treatment planning. ..
  61. Zhu X, Suk H, Lee S, Shen D. Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis. Brain Imaging Behav. 2019;13:27-40 pubmed publisher
  62. Cao X, Yang J, Gao Y, Guo Y, Wu G, Shen D. Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis. Med Image Anal. 2017;41:18-31 pubmed publisher
  63. Chen X, Zhang H, Lee S, Shen D. Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification. Neuroinformatics. 2017;15:271-284 pubmed publisher
    ..Experimental results confirm that our novel method outperforms the best single high-order FC network in diagnosis of mild cognitive impairment (MCI) subjects. ..
  64. Liu M, Zhang J, Yap P, Shen D. View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modality data. Med Image Anal. 2017;36:123-134 pubmed publisher
    ..e., MRI, PET, and CSF). Experimental results demonstrate that our method outperforms state-of-the-art methods that use incomplete multi-modality data for AD/MCI diagnosis. ..
  65. Zhang S, Zhao Y, Jiang X, Shen D, Liu T. Joint representation of consistent structural and functional profiles for identification of common cortical landmarks. Brain Imaging Behav. 2018;12:728-742 pubmed publisher
    ..Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified. ..
  66. Yang Z, Chen G, Shen D, Yap P. Robust Fusion of Diffusion MRI Data for Template Construction. Sci Rep. 2017;7:12950 pubmed publisher
    ..Experimental results show that our method yields diffusion MRI templates with cleaner fiber orientations and less artifacts caused by inter-subject differences in fiber orientation. ..
  67. Zhu X, Suk H, Wang L, Lee S, Shen D. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med Image Anal. 2017;38:205-214 pubmed publisher
    ..Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods. ..
  68. Hou Y, Park S, Wang Q, Zhang J, Zong X, Lin W, et al. Enhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering. Sci Rep. 2017;7:8569 pubmed publisher
    ..e., (1) vesselness-thresholding and (2) random forest classification. The experimental results show that the PVS segmentation performances can be significantly improved by using the enhanced and denoised 7 T MRI. ..