Daoqiang Zhang

Summary

Affiliation: Nanjing University of Aeronautics and Astronautics
Country: China

Publications

  1. pmc Temporally-constrained group sparse learning for longitudinal data analysis
    Daoqiang Zhang
    Dept of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
    Med Image Comput Comput Assist Interv 15:264-71. 2012
  2. pmc Multimodal classification of Alzheimer's disease and mild cognitive impairment
    Daoqiang Zhang
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
    Neuroimage 55:856-67. 2011
  3. pmc Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
    Daoqiang Zhang
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
    Neuroimage 59:895-907. 2012
  4. pmc Constrained sparse functional connectivity networks for MCI classification
    Chong Yaw Wee
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
    Med Image Comput Comput Assist Interv 15:212-9. 2012
  5. ncbi request reprint Minimizing joint risk of mislabeling for iterative Patch-based label fusion
    Guorong Wu
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
    Med Image Comput Comput Assist Interv 16:551-8. 2013
  6. pmc Tree-guided sparse coding for brain disease classification
    Manhua Liu
    IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
    Med Image Comput Comput Assist Interv 15:239-47. 2012
  7. doi request reprint Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification
    Chong Yaw Wee
    Image Display, Enhancement, and Analysis IDEA Laboratory, Biomedical Research Imaging Center BRIC and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
    Brain Struct Funct 219:641-56. 2014
  8. pmc Identification of MCI individuals using structural and functional connectivity networks
    Chong Yaw Wee
    Image Display, Enhancement, and Analysis IDEA Laboratory, Biomedical Research Imaging Center BRIC and Department of Radiology, University of North Carolina at Chapel Hill, NC, USA
    Neuroimage 59:2045-56. 2012
  9. doi request reprint Semisupervised kernel matrix learning by kernel propagation
    Enliang Hu
    Department of Mathematics, Yunnan Normal University, Kunming, China
    IEEE Trans Neural Netw 21:1831-41. 2010
  10. ncbi request reprint A multiobjective simultaneous learning framework for clustering and classification
    Weiling Cai
    Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
    IEEE Trans Neural Netw 21:185-200. 2010

Detail Information

Publications13

  1. pmc Temporally-constrained group sparse learning for longitudinal data analysis
    Daoqiang Zhang
    Dept of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
    Med Image Comput Comput Assist Interv 15:264-71. 2012
    ..We validate our method through estimation of clinical cognitive scores using imaging data at multiple time-points which are available in the Alzheimer's disease neuroimaging initiative (ADNI) database...
  2. pmc Multimodal classification of Alzheimer's disease and mild cognitive impairment
    Daoqiang Zhang
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
    Neuroimage 55:856-67. 2011
    ..Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers...
  3. pmc Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
    Daoqiang Zhang
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
    Neuroimage 59:895-907. 2012
    ..The results on both sets of experiments demonstrate that our proposed M3T learning scheme can achieve better performance on both regression and classification tasks than the conventional learning methods...
  4. pmc Constrained sparse functional connectivity networks for MCI classification
    Chong Yaw Wee
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
    Med Image Comput Comput Assist Interv 15:212-9. 2012
    ..Our results demonstrate that the constrained sparse network gives better classification performance than the conventional correlation-based network, indicating its greater sensitivity to early stage brain pathologies...
  5. ncbi request reprint Minimizing joint risk of mislabeling for iterative Patch-based label fusion
    Guorong Wu
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
    Med Image Comput Comput Assist Interv 16:551-8. 2013
    ..To demonstrate the performance of our proposed method, we have evaluated it on both whole brain parcellation and hippocampus segmentation, and achieved promising labeling results, compared with the state-of-the-art methods...
  6. pmc Tree-guided sparse coding for brain disease classification
    Manhua Liu
    IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
    Med Image Comput Comput Assist Interv 15:239-47. 2012
    ....
  7. doi request reprint Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification
    Chong Yaw Wee
    Image Display, Enhancement, and Analysis IDEA Laboratory, Biomedical Research Imaging Center BRIC and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
    Brain Struct Funct 219:641-56. 2014
    ..Furthermore, our proposed approach achieved similar classification performance when finer atlas was used to parcellate the brain space. ..
  8. pmc Identification of MCI individuals using structural and functional connectivity networks
    Chong Yaw Wee
    Image Display, Enhancement, and Analysis IDEA Laboratory, Biomedical Research Imaging Center BRIC and Department of Radiology, University of North Carolina at Chapel Hill, NC, USA
    Neuroimage 59:2045-56. 2012
    ..953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity...
  9. doi request reprint Semisupervised kernel matrix learning by kernel propagation
    Enliang Hu
    Department of Mathematics, Yunnan Normal University, Kunming, China
    IEEE Trans Neural Netw 21:1831-41. 2010
    ..The experiments demonstrate that KP is encouraging in both effectiveness and efficiency compared with three state-of-the-art algorithms and its related out-of-sample extensions are promising too...
  10. ncbi request reprint A multiobjective simultaneous learning framework for clustering and classification
    Weiling Cai
    Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
    IEEE Trans Neural Netw 21:185-200. 2010
    ..Empirical results on both synthetic and real data sets demonstrate the effectiveness and potential of MSCC...
  11. ncbi request reprint Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure
    Songcan Chen
    Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 PRC
    IEEE Trans Syst Man Cybern B Cybern 34:1907-16. 2004
    ..The experiments on the artificial and real-world datasets show that our proposed algorithms, especially with spatial constraints, are more effective...
  12. pmc Ensemble sparse classification of Alzheimer's disease
    Manhua Liu
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
    Neuroimage 60:1106-16. 2012
    ..85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images...
  13. ncbi request reprint Confidence-guided sequential label fusion for multi-atlas based segmentation
    Daoqiang Zhang
    Dept of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599
    Med Image Comput Comput Assist Interv 14:643-50. 2011
    ..e., majority voting and local weighted voting. Experimental results show that our sequential label fusion method can consistently improve the performance of both algorithms in terms of segmentation/labeling accuracy...