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- Claude Bernard -

“The joy of discovery is certainly the liveliest that the mind of man can ever feel”

PUBLICATIONS

Journal Publications

  1. Cai, H., Deng, R., Yang, D., Zhang, F. Wu, G., Chen, J., Harmonic Wavelet Neural Network for Discovering Neuropathological Propagation Patterns in Alzheimer's Disease, IEEE journal of Biomedical and Health Informatics, 2024.

  2. Dan, T., Dere, M., Kim, M., Kim, W., Wu, G., TauFlowNet: Revealing Latent Propagation Mechanism of Tau Aggregates Using Deep Neural Transport Equations,  Medical Image Analysis, 2024.

  3. Ma, H., Shi, Z., Kim M., Liu B., Smith, P., Liu, Y., Wu, G., Disentangling Sex-dependent Effect of APOE on Diverse Trajectory of Cognitive Decline in Alzheimer's Disease, vol. 292, Neuroimage (2024)

  4. Chen, J., Li, T., Zhao, B., Chen, H., Yuan, C., Garden, G., Wu, G., Zhu, H., The Interaction Effects of Age, APOE, and Common Environmental Risk Factors on Human Brain Structure, Cerebral Cortex (2023).

  5. Dan, T., Kim, M., Kim, W. H., Wu, G. Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics. IEEE transactions on medical imaging Pp, doi:10.1109/tmi.2023.3309821 (2023).

  6. Yu, Z., Shi, Z, Dere, M, Li, Q, Wu, G. Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer's Disease. Journal of Alzheimer's disease reports 7, 855-872, doi:10.3233/adr-230081 (2023).

  7. Cai, H. Sheng, X., Wu, G., Hu, B., Cheung, Y., Chen, J. Brain Network Classification for Accurate Detection of Alzheimer's Disease via Manifold Harmonic Discriminant Analysis. IEEE transactions on neural networks and learning systems Pp, doi:10.1109/tnnls.2023.3301456 (2023).

  8. Xu, E., Zhang J, Song, Q, Yang, D, Wu, G, Chen, M. Pathology steered stratification network for subtype identification in Alzheimer's disease. Medical Physics n/a, doi:https://doi.org/10.1002/mp.16655 (2023).

  9. Liu H, Cai H, Wu, G, Chen, J (2023) Learning Pyramidal Multi-Scale Harmonic Wavelets for Identifying the Neuropathology Propagation Patterns of Alzheimer's Disease, Medical Image Analysis.

  10. Liu H, Cai H, Wu, G, Chen, J (2023) Estimating Outlier-Immunized Common Harmonic Waves for Brain Network Analyses on the Stiefel Manifold, IEEE Journal of Biomedical and Health Informatics.

  11. Zhang J, Liu, Q, Zhang, H, Dai, M, Song, Q, Yang, D, Wu, G, Chen (2023), M, Uncovering the System Vulnerablity and Criticality of Human Brain Under Dynamical Neuropathological Events in Alzheimer’s Disease, Journal of Alzheimer’s Disease.

  12. Kyere F, Curtin I, Krupa O, McCormik, C, Dere M, Khan S, Kim M, Wang T, He Q, Wu G, Shih I, Sten, J (2022) Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy, Journal of Visual Experiments, DOI: 10.3791/64096

  13. Dan T, Huang Z, Cai H, Laurienti P, Wu G, “Learning Brain Dynamics of Evolving Manifold Functional MRI Data Using Geometric-Attention Neural Network”, IEEE Transaction on Medical Imaging, 2022.

  14. Dan T, Huang Z, Cai H, Lyday R, Laurienti P, Wu G, “Uncovering Shape Signatures of Resting-State Functional Connectivity by Geometric Deep Learning on Riemannian Manifold”, Human Brain Mapping, 2022.

  15. Chen J, Cai H, Yang D, Styner M, Wu G, “Characterizing the Propagation of Neuropathological Events of Alzheimer’s Disease Using Harmonic Wavelet Analysis”, Medical Image Analysis, 2022.

  16. Li W, Yang D, Yan C, Chen M, Li Q, Wu G, “Characterizing Network Selectiveness to the Dynamic Spreading of Neuropatholical Events in Alzheimer’s Disease”, Journal of Alzheimer’s Disease, 2022.

  17. Krupa O, Fragola G, Hadden-Ford E, Mory J, Liu T, Humphrey Z, Rees B, Krishnamurthy A, Snider W, Zylka M, Wu G, Xing L, Stein J, “NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images”, Cell Reports, 37(2), 2021.

  18. Hu D, Liu C, Kai X, Abramowitz, A, Wu G, “Characterizing the Resilience Effect of Neurodegeneration for the Mechanistic Pathway of Alzheimer's Disease Journal of Alzheimer’s Disease”, Journal of Alzheimer’s Disease, 2021.

  19. Hu R, Peng Z, Zhu X, Gan J, Zhu Y, Ma J, Wu G, “Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification”, IEEE Transaction on Medical Imaging, 2021.

  20. Yang D, Zhu X, Yan C, Peng Z, Bagonis M, Laurienti P, Styner M, Wu G, “Joint Hub Identification for Brain Networks by Multivariate Graph Inference”, Medical Image Analysis, 2021.

  21. Yang D, Chen J, Yan C, Kim M, Laurienti P, Styner M. Wu G, “Group-wise Hub Identification by Learning Common Graph Embeddings on Grassmannian Manifold”, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2021.

  22. Borland D, McCormik C, Patel N, Krupa O, Mory J, Beltran A, Farah T, Escobar-Tomlienovich C, Olson S, Kim M, Wu G, Stein J, “Segmentor: A tool for manual refinement of 3D microscopy annotations”, Bioinformatics, 2021.

  23. Gan J, Peng Z, Zhu X, Hu R, Ma J, Wu G, “Brain Functional Connectivity Analysis Based on Multi-Graph Fusion”, Medical Image Analysis, 71(7), 2021.

  24. Lin Y, Hou J, Yang D, Kim M, Laurienti P, Wu G, “Learning Dynamic Graph Embeddings for Accurate Detection of Cognitive State Changes in Functional Brain Networks”, Neuroimage, 230(4), 2021.

  25. Chen J, Han, G, Cai H, Yang D, Laurienti P, Styner M, Wu G, “Learning Common Harmonic Waves on Stiefel Manifold – A New Mathematical Approach for Brain Network Analysis”, IEEE Transaction on Medical Imaging, 40(1):419-430, 2021.

  26. Guo Y, Krupa O, Stein J, Wu G, Krishnamuthy A, “SAU-Net: A Unified Network for Cell Counting in 2D and 3D Microscopy Images”, IEEE/ACM Transaction on Computational Biology and Bioinformatics, 2021.

  27. Zhang Y, Hao Y, Xia K, and Wu G, A “Novel Computational Proxy for Characterizing Cognitive Reserve in Alzheimer's Disease”, Journal of Alzheimer’s Disease, 2020.

  28. Kim M, Chen Y, Yang D, Kaufer D, Wu G, “Constructing Connectome Atlas by Graph Laplacian Learning”, Neuroinformatics, 2020.

  29. Zhu Y, Zhu X, Kim M, Kaufer D, Wu G, “Long Range Early Diagnosis of Alzheimer’s Disease Using Longitudinal MR Imaging Data”, Medical Image Analysis, 67(1), 2021.

  30. Dong P, Guo Y, Gao Y, Liang P, Shi Y, Wu G, “Multi-Atlas Segmentation of Brain Anatomical Structures Using Hierarchical Hypergraph Learning”, IEEE Transaction on Neural Networks and Learning System, 2019.

  31. Mokhtari F, Akhlaghi M, Simpson S, Wu G, Laurienti P, “Sliding Window Correction Analysis: Modulating Window Shape for Dynamic Brain Connectivity in Resting State”, Neuroimage, 2019.

  32. Jia Y, Wang Z, Yang T, Li Y, Gao S, Wu G, T Jiang, P Liang, “Entorhinal Cortex Atrophy in Early, Drug-naïve Parkinson’s Disease with Mild Cognitive Impairment”, Aging and Disease, accepted.

  33. Zhu Y, Zhu X, Kim M, Kaufer D, Wu G, “Dynamic Hyper-Graph Inference Framework for Computer Assisted Diagnosis of Neurodegenerative Diseases”, IEEE Transaction on Medical Imaging, 2018 August.

  34. Tang, Y, , Zhu, C, “A First-order Splitting Method for Solving a Large-scale Composite Convex Optimization Problem”, Journal of Computational Mathematics, 2018, October.

  35. Zhou, L, Rekik, I, Yan, C, Wu G, “Editorial: High Performance Computing in Bio-medical Informatics”, Neuroinformatics, 2018 July.

  36. Zu C, Gao Y, Munsell B, Kim M, Peng Z, Cohen J, Zhang D, Wu G, “Identifying Disease-related Connectome Biomarkers by Sparse Hypergraph Learning”, Brain Imaging and Behavior, 2018 June.

  37. Mokhtari F, Rejeski W.J., Zhu Y, Wu G, Simpson S, Burdette J, Laurienti P, “Dynamic fMRI Networks Predict Success in a Behavior Weight Loss Program among Older Adults”, Neuroimage, 2018

  38. Adeli E, Thung K, An L, Wu G, Shi F, Wang T, and Shen D, “Semi-supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noise”, IEEE Transaction on Pattern Recognition and Machine Intelligence, 2018.

  39. Sanroma G, Benkarim O, Piella G, Camara O, Wu G, Shen D, Gispert J, Molinuevo J, and Ballester M, “Learning Non-linear Patch Embeddings with Neural Networks for Label Fusion”, Medical Image Analysis, 2018 Feb, 44(2):143-55.

  40. Wei L, Cao X, Wang Z, Gao Y, Hu S, Wang L, Wu G, and Shen D, “Learning-based Deformable Registration for Infant MRI by Integrating Random Forest with Auto-Context Model”, Medical Physics, 2017 October, 44(12): 6289-303.

  41. Cao X, Yang J, Gao Y, Guo Y, Wu G, and Shen D, “Dual-core Steered Non-rigid Registration for Multi-modal Images via Bi-directional Image Synthesis”, Medical Image Analysis, 2017 October, 41(10): 18-31.

  42. Wang Z, Zhu X, Adeli E, Zhu Y, Nie F, Munsell B, and Wu G, “Mutli-modal classification of neurodegenerative disease by progressive graph-based transductive learning”, Medical Image Analysis, 2017 July, 39(7): 218-30.

  43. Adeli E, Wu G, Saghafi B, An L, Shi F, and Shen D, "Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease", Scientific Reports, 2017 Jan, 7:41069.

  44. Hu S, Wei L, Gao Y, Guo Y, Wu G, Shen D, Learning-based Deformable Image Registration for Infant MR Images In the First Year of Life, Medial Physics, 2017 Jan, 44(1): 158-70.

  45. Song Y, Wu G, Bahrami K, Sun Q, Shen D, Progressive Multi-Atlas Label Fusion by Dictionary Evolution, Medical Image Analysis, 2017 Feb, 36(2): 162-71.

  46. Shen, D, Wu G, Suk H, Deep Learning in Medical Image Analysis, Annual Review of Biomedical Engineering, 2017 August, 19(8): 221-49.

  47. Zu C, Wang Z, Zhang D, Liang P, Shi Y, Shen D, Wu G, Robust Multi-Atlas Label Propagation by Deep Sparse Representation, Pattern Recognition, 2017 Mar, 63(3):511-7.

  48. Dong P, Wang L, Lin W, Shen D, Wu G, Scalable Joint Segmentation and Registration Framework for Infant Brain Images, Neurocomputing 2017 Mar, 229(3): 54-62.

  49. Zhang L, Wang Q, Gao Y, Li H, Wu G, Shen D, Concatenated Sparitally-localized Random Forests for Hippocampus Labeling in Adult and Infant MR Brain Images, Neurocomputing 2016 Mar, 229(3): 3-12.

  50. Zhang Y, Shi F, Wu G, Wang L, Yap P, Shen D, Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains, IEEE Tarns Medical Imaging, 2016 Dec, 35(12): 2568-77.

  51. Adeli E, Shi F, An L, Wee C, Wu G, Wang T, Shen D, Joint Feature-Sample Selection and Robust Diagnosis of Parkinson’s Disease from MRI data, Neuroimage, 2016 Nov, 141:11, 206-19.

  52. Zhang L, Wang Q, Gao Y, Wu G,  Shen D. Automatic labeling of MR brain images by hierarchical learning of atlas forests. Med Phys. 2016 Mar; 43(3): 1175.

  53. Zhang P, Wu G, Gao Y, Yap P, Shen D, A Dynamic Tree-based Registration Could Handle Possible Large Deformations among MR Brain Images. Compute Med Imaging Graph, 2016 Sep; 52:1-7.

  54. Wu G, Peng X, Ying S, Wang Q, Yap PT, Shen D, Shen D. EHUGS: Enhanced hierarchical unbiased graph shrinkage for efficient groupwise registration. PLoS One. 2016 Jan 22; 11(1): e0146870.

  55. Ma G, Gao Y, Wu G, Wu L, Shen D. Nonlocal atlas-guided multi-channel forest learning for human brain labeling. Med Phys. 2016 Feb; 43(2): 1003.

  56. Wu G, Kim M, Wang Q, Munsell B, Shen D. Scalable High Performance Image Registration Framework by Unsupervised Deep Learning of Feature Representations. IEEE Trans Biomed Eng. 2016 July, 63(7): 1505-13.

  57. Du S, Guo Y, Sanroma G, Ni D, Wu G, Shen D. Building dynamic population graph for accurate correspondence detection. Med Image Anal. 2015 Dec; 26(1):256-67.

  58. Han D, Gao Y, Wu G, Yap PT, Shen D. Robust anatomical landmark detection with application to MR brain image registration. Comput Med Imaging Graph. 2015 Dec; 46 Pt 3:277-90.

  59. Sanroma G, Wu G, Gao Y, Thung KH, Guo Y, Shen D. A transversal approach for patch-based label fusion via matrix completion. Med Image Anal. 2015 Aug; 24(1):135-48.

  60. Wu G, Kim M, Sanroma G, Wang Q, Munsell BC, Shen D; Alzheimer's Disease Neuroimaging Initiative. Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. Neuroimage. 2015 Feb 1; 106: 34-46.

  61. Shen D, Wu G, Zhang D, Suzuki K, Wang F, Yan P. Machine learning in medical imaging. Comput Med Imaging Graph. 2015 Apr; 41: 1-2.

  62. Wu Y, Wu G, Wang L, Munsell BC, Wang Q, Lin W, Feng Q, Chen W, Shen D. Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection. Med Phys. 2015 Jul; 42(7): 4174-89.

  63. Wang Q, Kim M, Shi Y, Wu G, Shen D; Alzheimer's Disease Neuroimaging Initiative. Predict brain MR image registration via sparse learning of appearance and transformation. Med Image Anal. 2015 Feb; 20(1): 61-75.

  64. Kim M, Wu G, Wang Q, Lee SW, Shen D. Improved image registration by sparse patch-based deformation estimation. Neuroimage. 2015 Jan 15;105: 257-68.

  65. Wee CY, Zhao Z, Yap PT, Wu G, Shi F, Price T, Du Y, Xu J, Zhou Y, Shen D. Disrupted brain functional network in internet addiction disorder: a resting-state functional magnetic resonance imaging study. PLoS One. 2014 Sep 16; 9(9): e107306.

  66. Sanroma G, Wu G, Gao Y, Shen D. Learning to rank atlases for multiple-atlas segmentation. IEEE Trans Med Imaging. 2014 Oct; 33(10): 1939-53.

  67. Min R, Wu G, Cheng J, Wang Q, Shen D; Alzheimer's Disease Neuroimaging Initiative. Multi-atlas based representations for Alzheimer's disease diagnosis. Hum Brain Mapping. 2014 Oct; 35(10): 5052-70.

  68. Shi F, Wang L, Wu G, Li G, Gilmore JH, Lin W, Shen D. Neonatal atlas construction using sparse representation. Hum Brain Mapping. 2014 Sep; 35(9): 4663-77.

  69. Wu G, Wang Q, Zhang D, Nie F, Huang H, Shen D. A generative probability model of joint label fusion for multi-atlas based brain segmentation. Medical Image Analysis. 2014 Aug; 18(6): 881-90.

  70. Bhavsar A, Wu G, Lian J, Shen D. Resolution enhancement of lung 4D-CT via group-sparsity. Med Phys. 2013 Dec; 40(12): 121717.

  71. Wang Q, Yap PT, Wu G, Shen D. Diffusion tensor image registration using hybrid connectivity and tensor features. Hum Brain Mapping. 2014 Jul; 35(7): 3529-46.

  72. Ying S, Wu G, Wang Q, Shen D. Hierarchical unbiased graph shrinkage (HUGS): a novel groupwise registration for large data set. Neuroimage. 2014 Jan 1; 84: 626-38.

  73. Kim M, Wu G, Li W, Wang L, Son YD, Cho ZH, Shen D. Automatic hippocampus segmentation of 7.0 Tesla MR images by combining multiple atlases and auto-context models. Neuroimage. 2013 Dec; 83: 335-45.

  74. Dai Y, Wang Y, Wang L, Wu G, Shi F, Shen D; Alzheimer’s Disease Neuroimaging Initiative. aBEAT: a toolbox for consistent analysis of longitudinal adult brain MRI. PLoS One. 2013; 8(4): e60344.

  75. Wu G, Wang Q, Lian J, Shen D. Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction. Med Phys. 2013 Mar; 40(3): 031710.

  76. Wu G, Kim M, Wang Q, Shen D. S-HAMMER: hierarchical attribute-guided, symmetric diffeomorphic registration for MR brain images. Hum Brain Mapping. 2014 Mar; 35(3): 1044-60.

  77. Wu G, Lian J, Shen D. Improving image-guided radiation therapy of lung cancer by reconstructing 4D-CT from a single free-breathing 3D-CT on the treatment day. Med Phys. 2012 Dec; 39(12): 7694-709.

  78. Guo Y, Wu G, Jiang J, Shen D. Robust anatomical correspondence detection by hierarchical sparse graph matching. IEEE Trans Med Imaging. 2013 Feb; 32(2): 268-77.

  79. Zhang Y, Yap PT, Wu G, Feng Q, Lian J, Chen W, Shen D. Resolution enhancement of lung 4D-CT data using multiscale interphase iterative nonlocal means. Med Phys. 2013 May; 40(5): 051916. .

  80. Dai Y, Shi F, Wang L, Wu G, Shen D. iBEAT: A toolbox for infant brain magnetic resonance image processing. Neuroinformatics. 2013 Apr; 11(2): 211-25.

  81. Liao S, Wu G, Shen D. A statistical framework for inter-group image registration. Neuroinformatics. 2012 Oct; 10(4): 367-78.

  82. Zhang Y, Wu G, Yap PT, Feng Q, Lian J, Chen W, Shen D. Hierarchical patch-based sparse representation--a new approach for resolution enhancement of 4D-CT lung data. IEEE Trans Med Imaging. 2012 Nov; 31(11): 1993-2005.

  83. Wang Q, Yap PT, Wu G, Shen D. Application of neuroanatomical features to tractography clustering. Hum Brain Mapping. 2013 Sep; 34(9): 2089-102.

  84. Kim M, Wu G, Shen D. Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching. Med Phys. 2012 Jan; 39(1): 353-66.

  85. Jia H, Wu G, Wang Q, Wang Y, Kim M, Shen D. Directed graph based image registration. Comput Med Imaging Graph. 2012 Mar; 36(2): 139-51.

  86. Li G, Nie J, Wu G, Wang Y, Shen D; Alzheimer's Disease Neuroimaging Initiative. Consistent reconstruction of cortical surfaces from longitudinal brain MR images. Neuroimage. 2012 Feb 15; 59(4): 3805-20.

  87. Liao S, Jia H, Wu G, Shen D; Alzheimer's Disease Neuroimaging Initiative. A novel framework for longitudinal atlas construction with groupwise registration of subject image sequences. Neuroimage. 2012 Jan 16; 59(2): 1275-89.

  88. Wu G, Wang Q, Shen D; Alzheimer's Disease NeuroImaging Initiative. Registration of longitudinal brain image sequences with implicit template and spatial-temporal heuristics. Neuroimage. 2012 Jan 2; 59(1): 404-21.

  89. Wu G, Jia H, Wang Q, Shen D. SharpMean: groupwise registration guided by sharp mean image and tree-based registration. Neuroimage. 2011 Jun 15; 56(4): 1968-81.

  90. Kim M, Wu G, Yap PT, Shen D. A general fast registration framework by learning deformation-appearance correlation. IEEE Trans Image Process. 2012 Apr; 21(4): 1823-33.

  91. Shi F, Yap PT, Wu G, Jia H, Gilmore JH, Lin W, Shen D. Infant brain atlases from neonates to 1- and 2-year-olds. PLoS One. 2011 Apr 14; 6(4): e18746.

  92. Wu G, Wang Q, Jia H, Shen D. Feature-based groupwise registration by hierarchical anatomical correspondence detection. Hum Brain Mapping. 2012 Feb; 33(2): 253-71.

  93. Li Y, Wang Y, Wu G, Shi F, Zhou L, Lin W, Shen D; Alzheimer's Disease Neuroimaging Initiative. Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features. Neurobiol Aging. 2012 Feb; 33(2): 427.e15-30.

  94. Jia H, Yap PT, Wu G, Wang Q, Shen D. Intermediate templates guided groupwise registration of diffusion tensor images. Neuroimage. 2011 Jan 15; 54(2): 928-39.

  95. Yap PT, Wu G, Shen D. Human Brain Connectomics: Networks, Techniques, and Applications. July 2010; 27(4): 131-134.

  96. Jia H, Wu G, Wang Q, Shen D. ABSORB: Atlas Building by Self-organized Registration and Bundling. Neuroimage. 2010 Jul 1; 51(3): 1057-70.

  97. Wang Q, Wu G, Yap PT, Shen D. Attribute vector guided groupwise registration. Neuroimage. 2010 May 1; 50(4): 1485-96.

  98. Yap PT, Wu G, Zhu H, Lin W, Shen D. F-TIMER: fast tensor image morphing for elastic registration. IEEE Trans Med Imaging. 2010 May; 29(5): 1192-203.

  99. Wu G, Yap PT, Kim M, Shen D. TPS-HAMMER: improving HAMMER registration algorithm by soft correspondence matching and thin-plate splines based deformation interpolation. Neuroimage. 2010 Feb 1; 49(3): 2225-33.

  100. Wang Q, Chen L, Yap PT, Wu G, Shen D. Groupwise registration based on hierarchical image clustering and atlas synthesis. Hum Brain Mapping. 2010 Aug; 31(8): 1128-40.

  101. Yap PT, Wu G, Zhu H, Lin W, Shen D. TIMER: tensor image morphing for elastic registration. Neuroimage. 2009 Aug 15; 47(2): 549-63.

  102. Tang S, Fan Y, Wu G, Kim M, Shen D. RABBIT: rapid alignment of brains by building intermediate templates. Neuroimage. 2009 Oct 1; 47(4): 1277-87. 

  103. Wu G, Qi F, Shen D. Learning-based deformable registration of MR brain images. IEEE Trans Med Imaging. 2006 Sep; 25(9): 1145-57.

Conference Publications

  1. Wei, Z, Dan, T, Ding, J, Wu, G, "NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes", Neural Information Processing Systems (NeurIPS), 2024.

  2. Wei, Z, Dan, T, Ding, J, Laurienti, P, Wu, G, "Representing Functional Connectivity with Structural Detour: A New Perspective to Decipher Structure-Function Coupling Mechanism", Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

  3. Ding, J, Dan, T, Wei, Z, Laurienti, P, Wu, G, "A Wasserstein Recipe for Replicable Machine Learning on Functional Neuroimages", Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

  4. Chow, C, Dan, T., Styner, M, Wu, G, "Understanding Brain Dynamics Through Neural Koopman Operator with Structure-Function Coupling", Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

  5. Huang, Y, Dan T, Kim, W, Wu, G, "Uncovering Cortical Pathways of Prion-like Pathology Spreading in Alzheimer's Disease by Neural Optimal Mass Transport", Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

  6. Hwang, Y, Hwang, S, Wu, G, Kim, W, "Multi-order Simplex-based Graph Neural Network for Brain Network Analysis", Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

  7. Baek, S, Sim, J, Wu, G, Kim, W, "OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels", Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

  8. Sim, J, Lee, M, Wu, G, Kim, W, "Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification", Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

  9. Dan, T, Wei, Z, Kim, W., Wu, G, "Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold", The 41th International Conference on Machine Learning (ICML 2024).

  10. Cho, H, Sim, J, Wu, G, Kim, W, "Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization", The 41th International Conference on Machine Learning (ICML 2024).

  11. Shi, Z, Dan, T, Smith, P, Wu, G, "Explainable Dementia Prediction Using Functional Neuroimages and Risk Factors", IEEE International Symposium on Biomedical Imaging (ISBI), 2024.

  12. Baek S, Sim, J., Dere, M, Wu, G, Kim, W, "Modality-Agnostic Style Transfer for Holistic Feature Imputation", IEEE International Symposium on Biomedical Imaging (ISBI), 2024.

  13. Sim, J, Jeon, S., Choi, I, Wu, G, Kim W, "Learning to Approximate Adaptive Kernel Convolution on Graphs", AAAI Conference on Artificial Intelligence (AAAI), 2024.

  14. Dan, T, Ding, J, Wei, Z, Kovalsky, S, Kim, M, Kim, W, Wu, G, “Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals”, Neural Information Processing Systems (NeurIPS), 2023.

  15. Wei, Z, Dan, T, Ding, J, Dere, M, Wu, G, “A General Stitching Solution for Whole-Brain 3D Nuclei Instance Segmentation from Microscopy Images”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  16. Park, J, Hwang Y, Kim, M, Chung, M, Wu, G, Kim, W, “Convolving Directed Graph Edges via Hodge Laplacian for Brain Network Analysis”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  17. Cho, H, Wu, G, Kim, W, “Mixing Temporal Graphs with MLP for Longitudinal Brain Connectome Analysis”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  18. Yang, D, Shen, H, Chen, M, Xue, Y, Wang, S, Wu, G, “Spatiotemporal Hub Identification in Brain Network by Learning Dynamic Graph Embedding on Grassmannian Manifold”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  19. Cai, H, Zhou, Z, Yang, D, Wu, G, Chen, J. “Discovering Brain Network Dysfunction in Alzheimer's Disease Using Brain Hypergraph Neural Network”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  20. Dan, T, Kim, M, Kim, W, Wu, G, “Enhance Early Diagnosis Accuracy of Alzheimer’s Disease by Elucidating Interactions between Amyloid Cascade and Tau Propagation”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  21. Dan, T, Kim, M, Kim, W, Wu, G, “TauFlowNet: Uncovering Propagation Mechanism of Tau Aggregates by Neural Transport Equation”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  22. Dan, T, Kim, M, Kim, W, Wu, G, “Uncovering Structural-Functional Coupling Alterations for Neurodegenerative Diseases”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  23. Turja, M, Styner, M, Wu, G, “DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.

  24. Wei, Z, Wu, G, “A General Stitching Solution for Whole-Brain 3D Nuclei Instance Segmentation from Microscopy Images”, Medical Imaging with Deep Learning (MIDL), 2023

  25. Dan, T, Wu, G, “Uncovering Structural-Functional Coupling Alternations for Alzhermer’s Disease”, Medical Imaging with Deep Learning (MIDL), 2023

  26. Cho, H, Wu, G, Kim W, “Spatio-Temporal Multi-Layer Perceptron for Longitudinal Brain Connectome Analysis”, Annual Meeting of the Organization for Human Brain Mapping (OHBM) , 2023.

  27. Baek, S, Choi, I, Dere, M, Kim, M, Wu, G, Kim, W, “Learning Covariance-based Multi-scale Representation of NeuroImaging Measures for Alzheimer Classification”, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.

  28. Dan, T, Cai, H, Huang, Z, Wu, G, “OSR-Net: Ordinary Differential Equation based on Brain State Recognition Neural Neowork”, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.

  29. Wei Z, Dan T, Ding J, McCormic C, Kyere F, Kim M, Borland D, Stein J, Wu G, “High Throughput Deep Model of 3D Nuclei Instance Segmentation by Stereo Stitching Contextual Gaps”, IEEE Symposium on Biomedical Imaging (ISBI 2023).

  30. Liu, H, Dan, T, Huang, Z, Yang, D, Kim, W, Kim, M, Laurienti, P, Wu, G, HoloBrain: A Harmonic Holography for Stereotyped Brain Function, International Conference of Information Processing in Medical Imaging (IPMI 2023).

  31. Dan T, Wang H, Cai H, Laurienti, P, Wu G, “Neuro-RDM: An Explainable Neural Network Landscape of Reaction-Diffusion Model for Cognitive Task Recognition”, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), Singapore, September, 2022.

  32. Gan J, Zhu X, Wu G, “Dual-graph Learning Convlutional Network for Interpreable Alzheimer’s Disease Diagnosis”, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), Singapore, September, 2022.

  33. Chio I, Wu G, Kim W, “How Much to Aggregate: Leanning Adaptive Node-wise Scales on Graphs for Brain Networks”, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), Singapore, September, 2022.

  34. Yang D, Chen M, Wu G, “A Neuropathological Hub Identification for Alzheimer’s Disease via Joint Analysis of Topological Structure and Neuropathological Burder”, IEEE Symposium on Biomedical Imaging (ISBI 2022).

  35. Yang F, Meng R, Wu G, Kim W, “Disentangled Representation of Longitudinal β-Amyloid for AD via Sequential Graph Variational Autoencoder with Supervision”, IEEE Symposium on Biomedical Imaging (ISBI 2022).

  36. Dan T, Huang J, Cai H, Wu G, “Manifold Learning in Detecting the Transacions of Dynamic Functional Connectivities Boosts Brain State-Specific Recognition”, IEEE Symposium on Biomedical Imaging (ISBI 2022).

  37. Dan T, Huang J, Cai H, Laurienti P, Wu G, “Detecting Brain State Changes by Geometric Deep Learning of Functional Dynamics on Riemannian Manifold”, 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Strasbourg, France, September, 2021.

  38. Yang F, Meng R, Cho H, Wu G, Kim W, “Disentangle Sequential Graph Autoencoder for Preclinical Alzheimer’s Disease Characterizations from ADNI study”, 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Strasbourg, France, September, 2021.

  39. Turja A, Yang D, Wu G, Styner M, “Learning the Latent Heat Diffusion Process through Structural Brain Network from Longitudinal β-Amyloid Data”, Medical Imaging with Deep Learning, MIDL 2021.

  40. Chen J, Yang D, Cai H, Styner M, Wu G, “Discovering Spreading Pathways of Neuropathological Events in Alzheimer’s Disease Using Harmonic Wavelets”, 27th International Conference on Information Processing in Medical Imaging (IPMI), June, 2021.

  41. Ma J, Krupa O, Kim M, Borland D, Stein J, Wu G, “3D Nucleus Instance Segmentation for Whole-Brain Microscopy Images”, 27th International Conference on Information Processing in Medical Imaing (IPMI), June, 2021.

  42. Ma X, Wu G, Hwang SJ, Kim W, “Multi-resolution Edge-wise Embedding of Graphs for Discovering Brain Network Dysfunction in Neurlogical Disorders”, 27th International Conference on Information Processing in Medical Imaing (IPMI), June, 2021.

  43. Mirani J, Fulmer N, Turja A, Wu G, Styner M, “Extra Axial Cerebrospinal Fluid Volume and a Diagnosis of Alzheimer’s Disease”, SPIE Medical Imaging, 2021.

  44. Zhen A, Kim M, Wu G, “Disentangling the Spatio-temporal Heterogeneity of Alzheimer’s Disease Using a Deep Predictive Stratification Network”, IEEE International Symposium on Biomedical Imaging (ISBI) 2021, Nice, France.

  45. Kim M, Yang D, Wu G, “Discovering Unprecedented Heuristics for Hub Identification by Joint Graph Embedding and Reinformcement Learning”, IEEE International Symposium on Biomedical Imaging (ISBI) 2021, Nice, France.

  46. Kim M, Wu G, “Constructing Reliable Network Biomarker Covariance by Joint Harmoniazation and Graph Learning”, IEEE International Symposium on Biomedical Imaging (ISBI) 2021, Nice, France.

  47. Liu Q, Yang D, Zhang J, Wei Z, Wu G, Chen M, “Analyzing the Spatiotemporal Interaction and Propagation of ATN Biomarkers in Alzheimer’s Disease Using Longitudinal Neruoimaging Data”, IEEE International Symposium on Biomedical Imaging (ISBI) 2021, Nice, France.

  48. Wu G, “Understanding the Propagation Pattern of Neuropathological Events Using Network Specific Harmonic Analysis”, SIAM Conference on Computational Science and Engineering 2021, Fort Worth, Texas, US.

  49. Zhang J, Yang D, He W, Wu G, and Chen M, “A Network-Guided Reaction-Diffusion Model of AT[N] Biomarkers in Alzheimer’s Disease”, 20th IEEE Internatinal Conference on BioInformatics and BioEngineering, Cincinnati, USA, October, 2020.

  50. Chen J, Han G, Cai H, Ma J, Kim M, Laurienti P, Wu G, “Estimating Common Harmonic Waves of Brain Networks on Stiefel Manifold”, 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru, October, 2020.

  51. Ma J, Zhu X, Yang D, Wu G, “Attention-Guided Deep Graph Neural Network for Longitudinal Alzheimer's Disease Analysis”, 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru.

  52. Lin Y, Laurienti P, Wu G, “Detecting Changes of Functional Connectivity by Dynamic Graph Embedding Learning”, 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru.

  53. Gan J, Zhu X, Hu R, Ma J, Peng Z, Wu G, "Multi-Graph Fusion for Functional Neuroimaging Biomarker Detection", International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan.

  54. Xie J, Li L, Yang D, Wu G, “Characterizing Network Resilience in Alzheimer’s Disease”, Organization for Human Brain Mapping (OHBM 2020), Montreal, Canada.

  55. Chen A, Yang D, Yan C, Kim M, Laurienti P, and Wu G, “Reinforcement Learning the Heuristics of Hub Identification over Brain Networks”, Organization for Human Brain Mapping (OHBM 2020), Montreal, Canada.

  56. Yang D, Hu D, Styner M, Wu G, “Discovering Propagation Pattern of Neurodegeneration across Brain Networks”, Organization for Human Brain Mapping (OHBM 2020), Montreal, Canada.

  57. Hou J, Yang D, Turja M, Sytner M, and Wu G, “Enhancing the Statistical Power of Tracking Network Alterations Using Longitudinal Network Analysis”, Organization for Human Brain Mapping (OHBM 2020), Montreal, Canada.

  58. Lin Y, Yang D, Peng J, Yan C, Gao Y, Kim M, Laurienti P, Wu G, “A General Learning-based Framework to Characterize Intrinsic Connectivity Strength in Brain Network”, Organization for Human Brain Mapping (OHBM 2020), Montreal, Canada

  59. Chen A, Yang D, Yan C, Peng Z, Kim M, Laurienti P, Wu G, “A Novel Spatial-Temporal Hub Identification Method for Dynamic Functional Networks”, IEEE International Symposium on Biomedical Imaging (ISBI) 2020, Iowa City, USA.

  60. Leinwand B, Wu G, Pipiras V, “Characterizing Frequency-Selective Network Vulnerability for Alzheimer’s Disease by Identifying Critical Harmonic Patterns”, IEEE International Symposium on Biomedical Imaging (ISBI) 2020, Iowa City, USA.

  61. Wang Y, Yang D, Li Q, Kaufer D, Styner M, Wu G, “Characterizing the Propagation Pattern of Neurodegeneration in Alzheimer’s Disease by Longitudinal Network Analysis”, IEEE International Symposium on Biomedical Imaging (ISBI) 2020, Iowa City, USA.

  62. Ma X, Wu G, Kim W, “Enriching Statistical Inferences on Brain Connectivity for Alzheimer’s Disease Analysis via Latent Space Graph Embedding”, IEEE International Symposium on Biomedical Imaging (ISBI) 2020, Iowa City, USA.

  63. Kim M, Moussa, A, Liang P, Kaufer D, Laurienti P, Wu G, “Revealing Functional Connectivity by Learning Graph Laplacian”, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October, 2019.

  64. Kim M, Zhu X, Peng Z, Liang P, Kaufer D, Laurienti P, Wu G, “Constructing Multi-scale Connectome Atlas by Learning Common Topology of Brain Networks”, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October, 2019.

  65. Turja A, Styner M, Wu G, “Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning”, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October, 2019.

  66. Yang D, Yan C, Nie F, Zhu X, Turja A, Zsembik L, Styner M, Wu G, “Joint Identification of Network Hub Nodes by Multivariate Graph Interfence”, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October, 2019.

  67. Guo Y, Stein, J, Wu G, Krishnamurthy, A, “SAU-Net: A Universal Deep Network for Cell Counting”, 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Niagara Falls, NY, Sept, 2019.

  68. Guo Y, Wang Q, Krupa O, Stein J, Wu G, Bradford K, Krishnamurthy A, “Cross Modality Microscopy Segmentation via Adversarial Adaption”, 7th International Conference on Bioinformatics and Biomedical Engineering, Granada, Spain, May, 2019.

  69. Kaufer D, Hatfield L, Bateman J, Harris M, Wu G, “Neuropsychiatric Profiles of Frontotemporal Degeneration Subtypes from the Neuroimaging in Frontotemporal Dementia (NIFD) Cohort”, 11th International Conference on Frontotemporal Dementias, Sydney, Australia, November, 2018.

  70. Moussa A, and Wu G, “Visualizing Human Brain Connectome in Virtual Reality”, IEEE International Symposium on Biomedical Imaging 2018, Washington, D.C., April, 2018.

  71. Huang H, Yan C, and Wu G, “A High Throughput Multi-Atlas Patch Based Segmentation Software”, IEEE International Symposium on Biomedical Imaging 2018, Washington, D.C., April, 2018.

  72. Wang Q, Krupa O, Stein J, and Wu G, “Accurate Segmentation of Clumped Cells in Mouse Brain Microscopy Images Using Adaptive Cascaded Convolutional Neural Network”, IEEE International Symposium on Biomedical Imaging 2018, Washington, D.C., April, 2018.

  73. Wang Q, Wang S, Liu T, Humphrey Z, Ghukasyan V, Conway M, Scott E, Fragola G, Bradford K, Zylka M, Krishnamurthy A, Stein J, Wu G, “Accurate and High Throughput Cell Segmentation Method for Mouse Brain Nuclei Using Cascaded Convolutional Neural Network”, 3rd International Workshop on Patch-based Technology in Medical Imaging, Quebec City, Canada, September, 2017.

  74. Zhu Y, Zhu X, Kim M, Kaufer D, Wu G, Personalized Diagnosis for Alzheimer’s Disease, MICCAI 2017, Quebec City, Canada, September, 2017.

  75. Zhu Y, Zhu X, Kim M, Kaufer D, Wu G, A Novel Dynamic Hyper-Graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases, IPMI 2017, Boone, NC, USA, June, 2017.

  76. Zhu Y, Zhu X, Kim M, Wu G, A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity, IPMI 2017, Boone, NC, USA, June, 2017.

  77. Dong P, Cao X, Zhang J, Kim M, Wu G, and Shen D, “Efficient Groupwise Registration for Brain MRI by Fast Initialization”, MLMI 2017, Quebec, Canada, September, 2017.

  78. Zu C, Gao Y, Munsell B, Kim M, Peng Z, Zhu Y, Gao W, Zhang D, Shen D, Wu G, Learning Subnetwork Biomarkers via Hypergraph for Classification of Autism Disease, ISMRM, Hawaii, USA, April, 2017.

  79. Guo Y, Dong P, Wu G, Lin W, Shen D, Longitudinal Hypergraph Learning: A Consistent Segmentation Method for Measuring the Growth Trajectory of Infant Hippocampus from Brain MR Images, ISMRM, Hawaii, USA, April, 2017.

  80. Cao X, Yang J, Gao Y, Wu G, Shen D, Region-Adaptive Deformable Registration for MRI/CT Pelvic Images via Bi-directional Image Synthesis, ISMRM, Hawaii, USA, April, 2017.

  81. Adeli E, Wu G, Kim M, Shen D, Which one is a better marker for the Diagnosis of Parkinson’s Disease: T1 MRI or DTI, ISMRM, Hawaii, USA, April, 2017.

  82. Dong P, Guo Y, Gao Y, Liang P, Shi Y, Wang Q, Shen D, Wu G, Segment Deep Gray Matter Nucleus from MR Images: An Automatic Computational Tool for Early Diagnosis of Parkinson’s Disease, ISMRM, Hawaii, USA, April, 2017.

  83. Zhu Y, Zhu X, Zhang H, Gao W, Shen D, Wu G, Reveal Consistent Spatial-Temporal Patterns from Dynamic Functional Connectivity for Autism Spectrum Disorder Identification. MICCAI 2016, Athens, Greece, October, 2016.

  84. Zhu Y, Zhu X, Kim M, Shen D, Wu G, Early Diagnosis of Alzheimer’s Disease by Joint Feature Selection and Classification on Temporally Structured Support Vector Machine. MICCAI 2016, Athens, Greece, October, 2016.

  85. Wang Z, Zhu X, Adeli E, Zhu Y, Zu C, Nie F, Shen D, Wu G, Progressive Graph-based Transductive Learning for Multi-Modal Classification of Alzheimer’s Disease. MICCAI 2016, Athens, Greece, October, 2016.

  86. Munsell B, Wu G, Gao Y, Desisto N, Styner M, Identifying Relationships in Functional and Structural Connectome Data Using a Hypergraph Learning Method, MICCAI 2016, Athens, Greece, October, 2016.

  87. Cao X, Gao Y, Yang J, Wu G, Shen D, Learning-based Multimodal Image Registration for Prostate Cancer Radiation Therapy, MICCAI 2016, Athens, Greece, October, 2016.

  88. Ni D, Ji X, Gao Y, Cheng J, Wang H, Qin J, Lei B, Wang T, Wu G, Shen D, Automatic Cystocele Severity Grading in Ultrasound by Spatio-temporal Regression, MICCAI 2016, Athens, Greece, October, 2016.

  89. Wang L, Guo Y, Cao X, Wu G, Shen D, Consistent Multi-Atlas Hippocampus Segmentation for Longitudinal MR Brain Images with Temporal Sparse Representation, MICCAI workshop on Patch-based Techniques in Medical Imaging, Athens, Greece, October, 2016.

  90. Guo Y, Dong P, Hao S, Wang L, Wu G, Shen D, Automatic Segmentation of Hippocampus for Longitudinal Infant Brain MR image Sequence by Spatial-Temporal Hypergraph Learning, MICCAI Workshop on Machine Learning in Medical Imaging, Athens, Greece, October, 2016.

  91. Dong P, Guo Y, Gao Y, Liang P, Shi Y, Shen D, Wu G, Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning, MICCAI workshop on Patch-based Techniques in Medical Imaging, Athens, Greece, October, 2016.

  92. Zu C, Gao Y, Munsell B, Kim M, Peng Z, Zhu Y, Gao W, Zhang D, Shen D, Wu G, Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph, MICCAI Workshop on Machine Learning in Medical Imaging, Athens, Greece, October, 2016.

  93. Zhu X, Suk H, Thung K, Zhu Y, Wu G, Shen D, Joint Discriminative and Representative Feature Selection for Alzheimer’s Disease Diagnosis, MICCAI Workshop on Machine Learning in Medical Imaging, Athens, Greece, October, 2016.

  94. Wei L, Hu S, Gao Y, Cao X, Wu G, Shen D, Learning Appearance and Shape Evolution for Infant Image Registration in the First Year of Life, MICCAI Workshop on Machine Learning in Medical Imaging, Athens, Greece, October, 2016.

  95. Kim M, Wu G, Rekik I, Shen D, Dual-Layer Groupwise Image Registration for Consistent Labeling of Longitudinal Brain Images, MICCAI Workshop on Machine Learning in Medical Imaging, Athens, Greece, October, 2016.

  96. Zhang L, Wang Q, Gao Y, Wu G, Shen D. Automatic Hippocampus Labeling of Sub-Region Random Forests. MICCAI Workshop on Patch-based Techniques in Medical Imaging (Patch-MI), Munich, Germany. October, 2015.

  97. Rekik I, Li G, Wu G, Lin W, Shen D. Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework. MICCAI Workshop on Patch-based Techniques in Medical Imaging (Patch-MI), Munich, Germany. October, 2015.

  98. Wu G, Zhu X, Wang Q, Shen D. Image Super-Resolution by Supervised Adaption of Patchwise Self-Similarity Form High-Resolution Image. MICCAI Workshop on Patch-based Techniques in Medical Imaging (Patch-MI), Munich, Germany. October, 2015.

  99. Wang Q, Wu G, Shen D. Dual-Layer L1-Graph Embedding for Semi-Supervised Image Labeling. MICCAI Workshop on Patch-based Techniques in Medical Imaging (Patch-MI), Munich, Germany. October, 2015.

  100. Dong P, Guo Y, Gao Y, Shen D, Wu G. Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph. MICCAI Workshop on Patch-based Techniques in Medical Imaging (Patch-MI), Munich, Germany. October, 2015.

  101. Ge H, Wu G, Wang L, Gao Y, Shen D. Hierarchical Multi-modal Image Registration by Learning Common Feature Representations. MICCAI Workshop on Machine Learning in Medical Imaging (MLMI), Munich, Germany. October, 2015.

  102. Zhu X, Suk HI, Thung KH, Wu G, Shen D. Multi-View Classification for Identification of Alzheimer’s Disease. MICCAI Workshop on Machine Learning in Medical Imaging (MLMI), Munich Germany. October, 2015.

  103. Munsell BC, Vanderweyen D, Mintzer J, Mintzer O, Gajadhar A, Zhu X, Wu G, Joseph J. Identifying Abnormal Network Alternations Common to Traumatic Brain Injury and Alzheimer’s Disease Patients Using Functional Connectome Data. MICCAI Workshop on Machine Learning in Medical Imaging (MLMI), Munich, Germany. October, 2015.

  104. Zhang P, Wu G, Gao Y, Yap PT, Shen D. Dynamic Tree-Based Large-Deformation Image Registration for Multi-Atlas Segmentation. MICCAI Workshop on Medical Computer Vision: Algorithm for Big Data (bicMCV), Munich, Germany. October, 2015.

  105. Guo Y, Wu G, Yap PT, Jewells V, Lin W, Shen D. Segmentation of Infant Hippocampus Using Common Feature Representations Learned for Multimodal Longitudinal data. MICCAI, Munich, Germany. October, 2015.

  106. Ma G, Gao Y, Wu G, Wu L, Shen D. Non-local Atlas Guided Multi-Channel Forest Learning for Human Brain Labeling. MICCAI, Munich, Germany. October, 2015.

  107. Song Y, Wu G, Sun Q, Bahrami K, Li C, Shen D. Progressive Label Fusion Framework for Multi-Atlas Segmentation by Dictionary Evolution. MICCAI, Munich, Germany. October, 2015.

  108. Kim M, Wu G, Guo Y, Shen D. Joint Labeling of Multiple Regions of Interest (ROIs) by Enhanced Auto Context Models. 2015 IEEE International Symposium on Biomedical Imaging (ISBI), New York, NY. April, 2015.

  109. Bhavsar A, Wu G, Shen D. Motion-Guided Resolution Enhancement for Lung 4D-CT. International Conference on Control Automation, Robotics, and Vision (ICARCV), Singapore. 2014.

  110. Wu G, Shen D. Hierarchical Label Fusion with Multiscale Feature Representation and Label-specific Patch Partition. MICCAI 2014, Boston, MA. September 14-18, 2014.

  111. Guo Y, Wu G, Lin W, Shen D. Segmenting Hippocampus from Infant Brains by Sparse Patch Matching with Deep-Learned Features. MICCAI 2014, Boston, MA. September 14-18, 2014.

  112. Min R, Cheng J, Price T, Wu G, Shen D. Maximum-Margin based Representation Learning from Multiple Atlases for Alzheimer's Disease Classification. MICCAI 2014, Boston, MA. September 14-18, 2014.

  113. Han D, Gao Y, Wu G, Yap PT, Shen D. Robust Anatomical Landmark Detection for MR Brain Image Registration. MICCAI 2014, Boston, MA. September 14-18, 2014.

  114. Wu G, Wang L, Gilmore J, Lin W, Shen D. Joint Segmentation and Registration for Infant Brain Images. MICCAI Workshop on Medical Computer Vision: Algorithms for Big Data, Boston, MA. September 14-18, 2014.

  115. Ma G, Gao Y, Wu G, Wang L, Shen D. Atlas-guided Multi-channel Forest Learning or Human Brain Labeling. MICCAI Workshop on Medical Computer Vision: Algorithms for Big Data, Boston, MA. September 14-18, 2014.

  116. Wang Q, Wu G, Wang L, Lin W, Shen D. Sparsity-Learning-Based Longitudinal MR Image Registration for Early Brain Development. MICCAI Workshop on Machine Learning in Medical Imaging (MLMI 2014), Boston, MA. September 14-18, 2014.

  117. Zhang L, Wang Q, Gao Y, Wu G, Shen D. Hierarchical Learning of Atlas Forests for Automatic Labeling of MR Brain Images. MICCAI Workshop on Machine Learning in Medical Imaging (MLMI 2014), Boston, MA. September 14-18, 2014.

  118. Sanroma G, Wu G, Thung KH, Guo Y, Shen D. Novel Multi-Atlas Segmentation by Matrix Completion. MICCAI Workshop on Machine Learning in Medical Imaging (MLMI 2014), Boston, MA. September 14-18, 2014.

  119. Sanroma G, Wu G, Gao Y, Shen D. Learning-Based Atlas Selection for Multiple Atlas Segmentation. CVPR 2014, Columbus, OH. June 24-27, 2014.

  120. Wu G, Kim M, Wang Q, Liao S, Gao Y, Shen D. Unsupervised Deep Feature Learning for Deformable Image Registration of MR Brains. MICCAI, Nagoya, Japan. September 22-26, 2013.

  121. Wu G, Nie F, Wang Q, Liao S, Zhang D, Shen D. Minimizing Joint Risk of Mislabeling for Iterative Patch-based Label Fusion. MICCAI 2013, Nagoya, Japan. September 22-26, 2013.

  122. Kim M, Wu G, Wang Q, Shen D. Brain-Cloud: A Generalized and Flexible Registration Framework for Brain MR Images. MICCAI Workshop on Medical Imaging on Augmented Reality (MIAR 2013), Nagoya, Japan. September 22-26, 2013.

  123. Kim M, Wu G, and Shen D, “Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images”, MICCAI Workshop on Machine Learning on Medical Imaging (MLMI 2013), Nagoya, Japan. September 22-26, 2013.

  124. Bhavsar A, Wu G, and Shen D, “Harnessing Group-Sparsity Regularization for Resolution Enhancement of Lung 4D-CT”, MICCAI 2013, Nagoya, Japan. September 22-26, 2013.

  125. Wang Q, Kim M, Wu G, and Shen D, “Joint Learning of Appearance and Transformation for Predicting Brain MR Image Registration”, IPMI 2013, Asilomar, California, USA, Jun. 29-July.3, 2013.

  126. Ying S, Wu G, Liao S, and Shen D, “Inter-Group Image Registration by Hierarchical Graph Shrinkage”, ISBI 2013, San Francisco, USA.

  127. Ying S, Wu G, Wang Q, Shen D, “Groupwise Registration via Graph Shrinkage on the Image Manifold”, CVPR, June 25-27, 2013, Oregon, USA

  128. Wu G, Wang Q, Zhang D, Shen D, “Robust Patch-Based Multi-Atlas Labeling by Joint Sparsity Regularization”, STMI 2012, Nice, France. (Best paper award)

  129. Wu G, Kim M, Wang Q, and Shen D, "Hierarchical Attribute-Guided Symmetric Diffeomorphic Registration for MR Brain Images", MICCAI 2012, Nice France.

  130. Shi Y, Wu G, and Shen D, "Dense Deformation Reconstruction via Sparse Coding", MIML 2012, Nice France.

  131. Liao S, Zhang D, Yap PT, Wu G, and Shen D, "Group Sparsity Constrained Automatic Brain Label Propagation", MIML 2012, Nice France.

  132. Kim M, Wu G, and Shen D, "Sparse Patch-guided Deformation Estimation for Improved Image Registration", MIML 2012, Nice France.

  133. Zhang D, Guo M, Wu G, and Shen D, "Sparse Patch-based Label Fusion for Multi-Atlas Segmentation", MBIA 2012, Nice France.

  134. Guo Y, Wu G, Dai Y, Jiang J, and Shen D, "Robust Anatomical Correspondence Detection by Graph Matching with Sparsity Constraint", MCV 2012, Nice France.

  135. Shi F, Wang L, Wu G, Zhang Y, Liu M, Gilmore J, Lin W, and Shen D, "Super-Resolution Atlas Construction Using Group Sparsity", MICCAI 2012, Nice France.

  136. Zhang T, Wu G, Yap PT, Feng Q, Lian J, Chen W, and Shen D, "Non-local Mean Resolution Enhancement of Lung 4D-CT Data", MICCAI 2012, Nice France.

  137. Wu G, Kim M, Wang Q, and Shen D, "S-HAMMER: Hierarchical Attribute-Guided Symmetric Diffeomorphic Registration", Human Brain Mapping 2012, Beijing, China.

  138. Zhang Y, Wu G, Yap PT, Feng Q, Lian J, Chen W, Shen D, "Reconstruction of Super-Resolution Lung 4D-CT Using a Patch-Based Sparse Representation", CVPR 2012, Rhode Island, USA.

  139. Wu G, Wang Q, Lian J, and Shen D, "Estimating the 4D Respiratory Lung Motion by Spatiotemporal Registration and Building Super-Resolution Image", MICCAI 2011.

  140. Zhang D, Wu G, Jia H, and Shen D, "Confidence-Guided Sequential Label Fusion for Multi-Atlas Based Segmentation", MICCAI 2011.

  141. Wang Q, Yap PT, Wu G, and Shen D, "Fiber Modeling and Clustering Based on Neuroanatomical Features", MICCAI 2011.

  142. Wang Q, Yap PT, Wu G, and Shen D, "Diffusion Tensor Image Registration with Combined Tract and Tensor Features", MICCAI 2011.

  143. Wu G, Wang Q, Lian J, and Shen D, "Reconstruction of 4D-CT from Single Free-Breathing 3D-CT for Image Guided Lung Radiotherapy", AAPM 2011.

  144. Wu G, Wang Q, Lian J, and Shen D, "Reconstruction of 4D-CT from a Single Free-Breathing 3D-CT by Spatial-Temporal Image Registration", IPMI 2011, Germany, 2011.

  145. Liao S, Jia H, Wu G, and Shen D, "A Novel Longitudinal Atlas Construction Framework by Groupwise Registration of Subject Image Sequences", IPMI 2011, Germany, 2011.

  146. Kim M, Wu G, and Shen D, "Groupwise Registration of Breast DCE-MR Image for Accurate Tumor Measurement", ISBI 2011, Chicago, USA, 2011.

  147. Jia H, Wu G, Wang Q, Kim M, and Shen D, "iTREE: Fast and Accurate Image Registration Based on the Combinative and Incremental Tree'", ISBI 2011, CHicago, USA, 2011.

  148. Wu G, Wang Q, Jia H, and Shen D, “Registration of Longitudinal Image Sequences with Implicit Template and Spatial-Temporal Heuristics”, MICCAI 2010, Beijing, China, 2010.

  149. Wu G, Jia H, Wang Q, and Shen D, “Groupwise Registration with Sharp Mean”, MICCAI 2010, Beijing, China, 2010.

  150. Wu G, Wang Q, Jia H, and Shen D, “Groupwise Registration by Hierarchical Anatomical Correspondence Detection”, MICCAI 2010, Beijing, China, 2010.

  151. Kim M, Wu G, Yap PT, and Shen D, “A Generalized Learning Based Framework for Fast Brain Image Registration”, MICCAI 2010, Beijing, China, 2010.

  152. Wang Q, Yap PT, Jia H, Wu G, and Shen D, “Hierarchical Fiber Clustering Based on Multi-Scale Neuroanatomical Features”, MIAR 2010, Beijing, China, 2010.

  153. Jia H, Wu G,Wang Q, Shen D, “ABSORB: Atlas Building by Self-Organized Registration and Bundling”, CVPR 2010, San Francisco, CA, 2010. 

  154. Wu G, Yap PT, Wang Q, Shen D, “Groupwise Registration from Exemplar to Group Mean: Extending HAMMER to Groupwise Registration”, ISBI 2010, The Netherlands, 2010.

  155. Yap PT, Wu G, Zhu H, Lin W, Shen D, “Fast Tensor Image Morphing for Elastic Registration”, MICCAI 2009, London, UK, 2009.

  156. Wang Q, Yap PT, Wu G, Shen D, “Attribute Vector Guided Groupwise Registration”, MICCAI 2009, London, UK, 2009.

  157. Yap PT, Wu G, Zhu H, Lin W, Shen D, “TIMER: Tensor Image Morphing for Elastic Registration”, MMBIA 2009, Miami Beach, Florida, 2009.

  158. Wu G, Qi F, Shen D, “Learning Best Features and Deformation Statistics for Hierarchical Registration of MR Brain Images”, IPMI 2007, The Netherlands, 2007.

  159. Wu G, Qi F, Shen D, “A General Learning Framework for Non-rigid Image Registration”, International Workshop on Medical Imaging and Augmented Reality (MIAR'06), Shanghai, China, 2006. 

  160. Wu G, Qi F, Shen D, “Improve Brain Registration Using Machine Learning Methods”, Third IEEE International Symposium on Biomedical Imaging (ISBI 2006), Arlington, VA, USA, 2006.

  161. Wu G, Qi F, Shen D, “Learning Best Features for Deformable Registration of MR Brains”, MICCAI 2005, Palm Springs, California, USA, 2005.

Books and Book Chapters

  1. Wu G, Laurienti P, Bonilha L, Munsell, B, “Connectomics in Medical Imaging” (Lecture Notes in Computer Science, vol. 10511), Proceedings of CNI 2017, Springer-Verlag, Berlin, 2017.

  2. Sanroma G, Wu G, Coupe P. Zhan Y, Munsell, B, Ruckert D, “Patch-based Techniques in Medical Imaging” (Lecture Notes in Computer Science, vol. 10530), Proceedings of Patch-MI 2017, Springer-Verlag, Berlin, 2017.

  3. Wu G, Shen, D, Sabuncu M, “Machine Learning in Medical Imaging”, Elsevier, 2016.

  4. Wu G, Coupe P. Zhan Y, Munsell, B, Ruckert D, “Patch-based Techniques in Medical Imaging” (Lecture Notes in Computer Science, vol. 9993), Proceedings of Patch-MI 2016, Springer-Verlag, Berlin, 2016.

  5. Wu G, Coupe P, Zhan Y, Munsell, B, Ruckert D, “Patch-based Techniques in Medical Imaging”(Lecture Notes in Computer Science, vol. 9467), Proceedings of Patch-MI 2015, Springer-Verlag, Berlin, 2015.

  6. Shen D, Wu G, Zhang D, Suzuki K, Yan P, Wang F, “Special Issue on Machine Learning in Medical Imaging”, Computerized Medical Imaging and Graphics, 2014.

  7. Wu G, Zhang D, and Zhou L, “Machine Learning in Medical Imaging”, (Lecture Notes in Computer Science, vol. 8679), Proceeding of MLMI 2014, Springer-Verlag, Berlin, 2014.

  8. Wu G, Zhang D, Shen D, Yan P, Suzuki K, Wang F, “Machine Learning in Medical Imaging”, (Lecture Notes in Computer Science, vol. 8184), Proceeding of MLMI 2013, Springer-Verlag, Berlin, 2013.

  9. Wu G, Wang Q, Jia H, Shi F, Yap P, Shen D, “The Emergence of Groupwise Registration in MR Brain Study”, Handbook of Biomedical Signal Processing 2011 (Chapter 9).

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University of North Carolina at Chapel Hill

Advanced Computational Medicine Laboratory

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