1. Wu, Y., Hong, Y., Feng, Y., Shen, D., Yap, P.-T., 2019. Mitigating Gyral Bias in Cortical Tractography via Asymmetric Fiber Orientation Distributions. Medical Image Analysis. (Best paper award, 2 runners-up)

  2. Wu, Y., Hong, Y., Ahmad, S., Lin, W., Shen, D., Yap, P,-T., Consortium, U.B.C.P., others, 2020. Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles, in: In International Conference on Medical Image Computing and Computer-Assisted Intervention. (Oral Presentation, NIH Award)

  3. Wu, Y., Hong, Y., Ahmad, S., Chang, W., Lin, W., Shen, D., Yap, P,-T. 2020. Globally Optimized Super-Resolution of Diffusion MRI Data via Fiber Continuity, in: In International Conference on Medical Image Computing and Computer-Assisted Intervention. (Oral Presentation, NIH Award))

  4. Wu, Y., Zhang, F., Makris, N., Ning, Y., Norton, I., She, S., Peng, H., Rathi, Y., Feng, Y., Wu, H., others, 2018d. Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder. NeuroImage 181, 16–29.

  5. Wu, Y., Ahmad, S., Huynh, K.M., Liu, S., Thung, KH., Lin, W., P.-T. Yap, 2021. An Automated Processing Pipeline for Diffusion MRI in the Baby Connectome Project, ISMRM, May 15-20, 2021. (Summa Cum Laude Award)

  6. Wu, Y., Hong, Y., Ahmad, S., Yap, P,-T., Consortium, U.B.C.P., others, 2021. Active Cortex Tractography, in: International Conference on Medical Image Computing and Computer-Assisted Intervention. (Early Accept)

  7. Wu, Y., Ahmad, S., Yap, P,-T., 2021. Highly Reproducible Whole Brain Parcellation in Individuals via Voxel Annotation with Fiber Clusters, in: International Conference on Medical Image Computing and Computer-Assisted Intervention.

  8. Wu, Y., Hong, Y., Lin, W., and Yap, P.T. Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020. (Oral Presentation)

  9. Wu, Y., Hong, Y., and Yap, P.T. Mitigating Gyral Bias via Active Cortex Tractography, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020. (Oral Presentation)

  10. Wu, Y., Ahmad, S., Ma, L., Yang, E., P.-T. Yap, 2021. Subsampling Diffusion Gradients via Poisson Sphere Elimination, ISMRM, May 15-20, 2021.

  11. Wu, Y., Ahmad, S., Lin, W., Yap, P.-T., 2021. ``White Matter Tract Atlases of a Century of Human Life'', in: OHBM, Virtual Meeting, 21-25 June, 2021. (Merit Abstract Award)

  12. Wu, Y., Lin, W., Shen, D., Yap, P.-T., Consortium, U.B.C.P., others, 2019d. Asymmetry Spectrum Imaging for Baby Diffusion Tractography, in: International Conference on Information Processing in Medical Imaging. Springer, pp. 319–331. (Oral Presentation)

  13. Wu, Y., Feng, Y., Shen, D., Yap, P.-T., 2018a. A Multi-Tissue Global Estimation Framework for Asymmetric Fiber Orientation Distributions, in: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 45–52. (Oral Presentation, Early Accept)

  14. Wu, Y., Feng, Y., Li, F., Westin, C.F., 2015. Global consistency spatial model for fiber orientation distribution estimation, in: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE, pp. 1180–1183. (Oral Presentation)

  15. Wu, Y., Hong, Y., Lin, W., and Yap, P.T. , the UNC/UMN Baby Connectome Project Consortium. White Matter Tract Atlases of the Baby Brain, OHBM, Montreal, Canada, June 26-July 3, 2020.

  16. Wu, Y., Hong, Y., Lin, W., and Yap, P.T. Model-Free, Fast, and Automated Correction of Diffusion Gradient Orientations, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020.

  17. Wu, Y., Hong, Y., Lin, W., and Yap, P.T. Automated Identification of Non-Brain Voxels for Clean Brain Extraction Using Diffusion MRI, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020.

  18. Wu, Y., Feng, Y., Li, F., Gao C., 2015. A Novel Fiber Orientation Distribution Reconstruction Method Based on Dictionary Basis Function Framework. Chinese Journal of Biomedical Engineering, (3), p.6.

  19. Wu, Y., Feng, Y., Shen, D., Yap, P.-T., 2018c. Penalized Geodesic Tractography for Mitigating Gyral Bias, in: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 12–19. (Early Accept)

  20. Wu, Y., Feng, Y., Shen, D., Yap, P.-T., 2018b. Asymmetric Orientation Distributions Mitigate Gyral Bias in Cortical Tractography, in: OHBM, Singapore, 17-21 June, 2018.

  21. Wu, Y., Lin, W., Shen, D., Yap, P.-T., 2019b. Improving Tractography in Baby Diffusion MRI via Asymmetric Spectrum Imaging, in: Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM).

  22. Wu, Y., Lin, W., Shen, D., Yap, P.-T., 2019c. The Effects of Fiber Response Functions on Orientation Estimation in Baby Diffusion MRI, in: OHBM, Rome, Italy, June 9-13, 2019.

  23. Wu, Y., Xu, Y., Feng, Y., Gao, C., Li, F., 2014. A new model-based spherical deconvolution method for multi-fiber reconstruction, in: 2014 9th IEEE Conference on Industrial Electronics and Applications. IEEE, pp. 1456–1460.

  24. Zhang, F., Wu, Y., Norton, I., Rathi, Y., Golby, A.J., O’Donnell, L.J., 2019b. Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Human brain mapping.

  25. Zhang, Fan, Wu, Y., Norton, I., Rigolo, L., Rathi, Y., Makris, N., O’Donnell, L.J., 2018. An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. Neuroimage 179, 429–447.

  26. Huynh, K.M., Xu, T., Wu, Y., Wang, X., Chen, G., Wu, H., Thung, K.H., Lin, W., Shen, D. and Yap, P.T., 2020. Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging. IEEE Transactions on Medical Imaging. (Co-First author)

  27. Feng, Y., Wu, Y., Rathi, Y., Westin, C.-F., 2015. Sparse deconvolution of higher order tensor for fiber orientation distribution estimation. Artificial intelligence in medicine 65, 229–238.

  28. Feng, Y., Wu, Y., G, Zhang., R Liang., 2015. High Order Tensor diffusion magnetic resonance sparse imaging based on compressed sensing. Pattern Recognition and Artificial Intelligence, 28(8):710-719.

  29. Huynh, K.M., Chen, G., Wu, Y., Shen, D., Yap, P.-T., 2019a. Multi-Site Harmonization of Diffusion MRI Data via Method of Moments. IEEE transactions on medical imaging.

  30. Zhang, F., Wu, Y., Norton, I., Rathi, A.J., Yogesh, Golby, O’Donnell, L.J., 2019a. White matter parcellation test-retest reproducibility of diffusion MRI tractography fiber clustering, in: Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM).

  31. Zhang, F, Wu, Y., Norton, I., Rathi, Y., Makris, N., O’Donnell, L., 2018. A data-driven groupwise fiber clustering atlas for consistent white matter parcellation and anatomical tract identification of subjects across the lifespan, in: In: Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

  32. Ahmad, S., Wu, Y., Huynh K., Thung, K,-H., Lin, W., Shen, D., Yap, P,-T. 2020. Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts, in: International Conference on Information Processing in Medical Imaging. (Accepted)

  33. Huynh K., Wu, Y., Thung, K,-H., Ahmad, S., Taylor, H., Shen, D., Yap, P,-T. 2020. Characterizing Intra-Soma Diffusion with Spherical Mean Spectrum Imaging, in: International Conference on Information Processing in Medical Imaging. (Accepted)

  34. Taylor, H., Ahmad, S, Wu, Y., Huynh, K., Zhou, Z., Wu, Z., Li, G., Lin, W., Wang L., Shen, D., Zhang H, Yap P.-T. “Iterative Longitudinal Infant Cortical Parcellation Using Multi-Modal Connectome Harmonics”, OHBM, Montreal, Canada, June 26- July 3, 2020.

  35. Huynh, K.M., Wu, Y., Thung, K.H., Ahmad, S., Taylor, H., Lin, W., Shen, D., Yap, P.T., “Quantifying Intra-Soma Diffusion Properties via Spherical Mean Spectrum Imaging”, 28th ISMRM, Paris, France, August 7-10, 2020.

  36. Huynh, K.M., Wu, Y., Taylor, H., Lin, W., Shen, D., Yap, P.T., “Tackling Degeneracy in Linear Tensor Encoding Diffusion MRI”, 28th ISMRM, Paris, France, August 7-10, 2020.

  37. Huynh, K.M., Ahmad, S., Wu, Y., Thung, K.H., Wu, Z., Lin, W., Zhang, H., Wang, L., Li, G., and Yap, P.T. “Correlation of Myelin Content and Neurite Density in the Early Developing Human Cortex”, OHBM, Montreal, Canada, June 26-30, 2020.

  38. Huynh, K.M., Wu, Y., Thung, K.H., Ahmad, S., Taylor, H., Lin, W., Shen, D., Yap, P.T., “Multivariate Quantification of Brain Development During the First Two Years of Life”, OHBM, Montreal, Canada, June 26-30, 2020.

  39. Huynh, K.M., Wu, Y., Thung, K.-H., Chen, G., Lin, W., Shen, D., Yap, P.-T., 2019b. Biases of Microstructure Models in Baby Diffusion MRI, in: Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM).

  40. Sun, P., Wu, Y., Chen, G., Wu, J., Shen, D., Yap, P.-T., 2018. Tissue Segmentation Using Sparse Non-negative Matrix Factorization of Spherical Mean Diffusion MRI Data, in: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 69–76.

  41. Huynh, K.M., Xu, T.,Wu, Y., Thung, K.H., Chen, G., Lin, W., Shen, D. and Yap, P.T., 2019, October. Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 556-563). Springer, Cham.

  42. Huynh, K.M., Xu, T.,Wu, Y., Chen, G., Thung, K.H., Wu, H., Lin, W., Shen, D., Yap, P.T. and UNC/UMN Baby Connectome Project Consortium, 2019, October. Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 547-555). Springer, Cham.

  43. Taylor IV, H.P., Wu, Z.,Wu, Y., Shen, D., Zhang, H. and Yap, P.T., 2019, October. Automated Parcellation of the Cortex Using Structural Connectome Harmonics. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 475-483). Springer, Cham.

  44. Huynh, K.M., Wu, Y., Chen, G., Thung, K.-H., Lin, W., Shen, D., Yap, P.-T., 2019. Quantifying Tissue Microstructure Non-Gaussianity in the Presence of Fiber Dispersion,105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019. (Oral Presentation)

  45. Nath, V., Schilling, K.G., Parvathaneni, P., Huo, Y., Blaber, J.A., Hainline, A.E., etc., Wu, Y., Barakovic, M., Romascano, D., Rafael‐Patino, J., Frigo, M. and Girard, G., 2019. Tractography reproducibility challenge with empirical data (traced): The 2017 ismrm diffusion study group challenge. Journal of Magnetic Resonance Imaging.

  46. Maier-Hein, K.H., Neher, P.F., Houde, J.C., Côté, M.A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.C., Lin, Y.C. ,ect., Wu, Y., Ji, Q. and Reddick, W.E., 2017. The challenge of mapping the human connectome based on diffusion tractography. Nature communications, 8(1), p.1349.

  47. Zhang, H., Palaniyappan, L., Wu, Y., Cong, E., Wu, C., Ding, L., Jin, F., Qiu, M., Huang, Y., Wu, Y. and Wang, J., 2020. The concurrent disturbance of dynamic functional and structural brain connectome in major depressive disorder: the prefronto-insular pathway. Journal of Affective Disorders.

  48. Jin, L., Zeng, Q., He, J., Feng, Y., Zhou, S. and Wu, Y., 2019. A ReliefF-SVM-based method for marking dopamine-based disease characteristics: A study on SWEDD and Parkinson’s disease. Behavioural brain research, 356, pp.400-407.

  49. Yue, L., Hu, D., Zhang, H., Wen, J., Wu, Y., Wang, T., Shen, D. and Xiao, S., 2019. Prediction of 7-year progression from subjective cognitive decline to MCI: evidence from the china longitudinal ageing study (CLAS). Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 15(7), pp.P1396-P1397.

  50. Li, G., Liu, Y., Zheng, Y., Wu, Y., Yap, P.-T., Qiu, S., Zhang, H., and Shen, D., 2019. Multiscale Modeling of Intra-Regional and Inter-Regional Connectivities and Their Alterations in Major Depressive Disorder, 105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019. (Oral Presentation)

  51. Hu, J.Q., Feng, Y.J., Zhou, S.Q., Huang, L.P., Zeng, Q.R., Wu, Y. and Li, Y.Q., 2017, May. An improved mass spring model based on internal point set domain constraint. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 6826-6831). IEEE.

  52. Li, G., Liu, Y., Zheng, Y., Wu, Y., Yap, P.T., Qiu, S., Zhang, H. and Shen, D., 2019, October. Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-State fMRI. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 682-690). Springer, Cham.

  53. Jiang, W., Zhang, H., Hsu, L.M., Hu, D., Li, G., Wu, Y. and Shen, D., 2019, October. Early Development of Infant Brain Complex Network. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 832-840). Springer, Cham.

  54. Jiao, Z., Huang, P., Kam, T.E., Hsu, L.M., Wu, Y., Zhang, H. and Shen, D., 2019, October. Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 620-628). Springer, Cham.

  55. Liu, F., Feng, J., Chen, G., Wu, Y., Hong, Y., Yap, P.T. and Shen, D., 2019. DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 620-628). Springer, Cham.

  56. Cao, Z., Jin, E., Zhou, S., Wu, Y., Li, Y. and Feng, Y., 2018, May. A Data-driven Voxel-wise White Matter Fiber Clustering Model Based on Priori Anatomical Data. In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) (pp. 65-70). IEEE.

  57. Xiao, C., Feng, Y., Li, Y., Zeng, Q., Zhang, J. and Wu, Y., 2017, May. Real-time and authentic blood simulation for surgical training. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 6832-6837). IEEE.

  58. Gao, C., Feng, Y., Wu, Y., Zhang, J., Xu, T. and Wang, Z., 2016, July. Swarm tracking approach for global probabilistic tractography with spherical deconvolution. In 2016 35th Chinese Control Conference (CCC) (pp. 4048-4053). IEEE.

  59. Zhang, J., Xu, T., Feng, Y., Wu, Y., Li, Y., He, J. and Zhou, S., 2016, May. A self-adaptive local feature extraction based magnetic resonance imaging. In 2016 Chinese Control and Decision Conference (CCDC) (pp. 6563-6567). IEEE.

  60. Hong, Y., Chang, W.-T., Chen, G., Wu, Y., Lin, W., Shen, D., Yap, P.-T. “50-Fold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)”, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020.

  61. Huynh, H., Wu, Y., Thung, KH., Chen, G., Lin, W., Shen, D., Yap, P.-T. “Dense Mapping of Microstructural Development in the Human Brain During the First Two Years of Life”, OHBM, Rome, Italy, June 9-13, 2019.

  62. Li, G., Liu, Y., Zheng, Y., Wu, Y., Yap, P.-T, Qiu, S. Zhang, H., Shen, D. “Aberrant Limbic-Executive Rather Than Default Mode-Salience System in Major Depressive Disorder”, OHBM, Montreal, Canada, June 26- July 3, 2020

  63. Chiara Maffei, Gabriel Girard Kurt G. Schilling, Nagesh Adluru, Dogu Baran Aydoğan, Andac Hamamci, Fang-Cheng Yeh, Matteo Mancini, Ye Wu, Alessia Sarica, Achille Teillac, Steven H. Baete Davood Karimi, Ying-Chia Lin Fernando Boada Nathalie Richard Bassem Hiba, Aldo Quattrone, Yoonmi Hong, Dinggang Shen, Pew-Thian Yap, Tommy Boshkovski, Jennifer S. W. Campbell, Nikola Stikov, G. Bruce Pike, Barbara B. Bendlin, Vivek Prabhakaran, Andrew L. Alexander, Adam Anderson, Bennett A Landman Erick J. Canales-Rodríguez, Muhamed Barakovic Jonathan Rafael-Patino, Thomas Yu, Gaëtan Rensonnet Simona Schiavi Alessandro Daducci, Marco Pizzolato, Elda Fischi-Gomez Jean-Philippe Thiran George Dai, Giorgia Grisot, Nikola Lazovski, Albert Puente, Matt Rowe, Irina Sanchez, Vesna Prchkovska, Robert Jones, Julia Lehman, Suzanne Haber, Anastasia Yendiki. "The IronTract challenge: Validation and optimal tractography methods for the HCP diffusion acquisition scheme", 28th ISMRM, Paris, France, August 7-10, 2020.

  64. Zhou, S., Jin, L., He, J., Zeng, Q., Wu, Y., Cao, Z. and Feng, Y., 2018. Distributed performance of white matter properties in chess players: A DWI study using automated fiber quantification. Brain research, 1700, pp.9-18.

  65. Xu, T., Feng, Y., Wu, Y., Zeng, Q., Zhang, J., He, J. and Zhuge, Q., 2017. A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals. PloS one, 12(1), p.e0168864.

  66. Xu, Y., Feng, Y., Niu, Y. and Wu, Y., 2014. Estimation of fiber orientation distribution with non-negative constrained higher order tensor deconvolution. Journal of Systems Science and Mathematical Sciences, (7), p.4.

  67. Hong, Y., Chang, W.T., Chen, G., Wu, Y., Lin, W., Shen, D. and Yap, P.T., 2020. Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE). arXiv preprint arXiv:2002.10908.

  68. Yue, L., Hu, D., Zhang, H., Wen, J., Wu, Y., Li, W., Sun, L., Li, X., Wang, J., Li, G. and Wang, T., 2020. Prediction of 7-year's Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment. medRxiv.

  69. Li, G., Liu, Y., Zheng, Y., Wu, Y., Li, D., Liang, X., Chen, Y., Cui, Y., Yap, P.T., Qiu, S. and Zhang, H., 2020. Multiscale Neural Modeling of Resting-state fMRI Reveals Executive-Limbic Malfunction as a Core Mechanism in Major Depressive Disorder. medRxiv.

  70. 冯远静, 吴烨, 张贵军, 梁荣华, 2015. 基于压缩感知高阶张量扩散磁共振稀疏成像方法. 模式识别与人工智能 28, 710–719.

  71. 吴烨, 冯远静, 李斐, 高成锋, 2015. 基于字典基函数框架的纤维方向分布模型重建. 中国生物医学工程学报 34, 297–307.

  72. 许优优, 冯远静, 牛延鹏, 吴烨, 2014. 高阶张量反卷积非负约束的纤维方向分布估计方法. 系统科学与数学 34, 805–814.