Conference Papers

  1. Takemaru L, Yang S, Wu R, He B, Davatzikos C, Yan J, Shen L. (2024) Mapping Alzheimer's disease pseudo-progression with multimodal biomarker trajectory embeddings. ISBI’24: IEEE Int. Sym. on Biomedical Imaging, in press, Athens, Greece, May 27-30, 2024.

  2. Wen Z, Bao J, Yang S, Wen J, Zhan Q, Cui Y, Erus G, Yang Z, Thompson P, Zhao Y, Davatzikos C,. Shen L. (2024) Multiscale estimation of morphometricity for revealing neuoranatomical basis of cognitive traits. ISBI’24: IEEE Int. Sym. on Biomedical Imaging, in press, Athens, Greece, May 27-30, 2024.

  3. Duong-Tran D, Magsino M, Goni J, Shen L. (2024) Preserving human large-scale brain connectivity fingerprint identifiability with random projections. ISBI’24: IEEE Int. Sym. on Biomedical Imaging, in press, Athens, Greece, May 27-30, 2024.

  4. Ataee Tarzanagh D, Nazari P, Hou B, Shen L, Balzano L. (2024) Online bilevel optimization: Regret analysis of online alternating gradient methods. AISTATS’24: The 27th International Conference on Artificial Intelligence and Statistics, in press, Valencia, Spain, May 2-4, 2024

  5. Hou B, Mondragon A, Ataee Tarzanagh D, Zhou Z, Saykin AJ, Moore JH, Ritchie MD, Long Q, Shen L. (2024) PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge. AMIA-IS’24: AMIA Informatics Summit, in press, Boston, MA, March 18-21, 2024. 

  6. Mu S, Bao J, Xu H, Shivakumar M, Yang S, Ning X, Kim D, Davatzikos C, Shou H, Shen L, for the ADNI. (2024) Multivariate mediation analysis with voxel-based morphometry revealed the neurodegeneration pathways from genetic variants to Alzheimer’s disease. AMIA-IS’24: AMIA Informatics Summit, in press, Boston, MA, March 18-21, 2024. [Marco Ramoni Distinguished Paper Award for Translational Bioinformatics]

  7. Wu R, He B, Hou B, Saykin AJ, Yan J, Shen L. (2024) Cluster analysis of cortical amyloid burden for identifying imaging-driven subtypes in mild cognitive impairment. AMIA-IS’24: AMIA Informatics Summit, in press, Boston, MA, March 18-21, 2024. 

  8. He W, Hou B, Demiris G, Shen L. (2024) Interpretability study for long interview transcripts from behavior intervention sessions for family caregivers of dementia patients. AMIA-IS’24: AMIA Informatics Summit, in press, Boston, MA, March 18-21, 2024. 

  9. Xu FH, Gao M, Chen J, Garai S, Duong-Tran DA, Zhao Y, Shen L. (2024) Topology-based clustering of functional brain networks in an Alzheimer’s disease cohort. AMIA-IS’24: AMIA Informatics Summit, in press, Boston, MA, March 18-21, 2024. 

  10. Xiang S, Lawrence PJ, Peng B, Chiang C, Kim D, Shen L, Ning X. (2024) Modeling path importance for effective Alzheimer's disease drug repurposing. PSB’24: Pac Symp Biocomput., in press, Big Island of Hawaii, January 3-7, 2024.

  11. Zhou Z, Ataee Tarzanagh D, Hou B, Tong B, Xu J, Feng Y, Long Q, Shen L. (2023) Fair canonical correlation analysis. NeurIPS’23: 37th Conference on Neural Information Processing Systems, New Orleans, LA, December 10-16, 2023. [26% acceptance rate] [PDF] from neurips.cc

  12. Pala D, Xie Y, Xu J, Zhang Y, Shen L. (2023) Causal effects of environmental exposures and biological traits on the difference between phenotypic and chronological ages. BIBM’23 13th International Workshop on Biomedical and Health Informatics, in press, Istanbul, Turkey, Dec 5-8, 2023.

  13. Zhou R, Zhou H, Shen L, Chen BY, Zhang Y, He L. (2023) Integrating multimodal contrastive learning and cross-modal attention for Alzheimer’s disease prediction in brain imaging genetics. BIBM’23: IEEE Int. Conf. on Bioinformatics and Biomedicine, in press, Istanbul, Turkey, Dec 5-8, 2023. [19.5% acceptance rate for long papers]

  14. Zhou H, Zhang Y, He L, Shen L, Chen BY. (2023) Interpretable graph convolutional network for Alzheimer’s disease diagnosis using multi-modal imaging genetics. BIBM’23: IEEE Int. Conf. on Bioinformatics and Biomedicine, in press, Istanbul, Turkey, Dec 5-8, 2023. [19.7% acceptance rate for short papers]

  15. Tong B, Zhou Z, Ataee Tarzanagh D, Hou B, Saykin AJ, Moore J, Ritchie M, Shen L. (2023) Class-balanced deep learning with adaptive vector scaling loss for dementia stage detection. MLMI’23: The 14th International Workshop on Machine Learning in Medical Imaging, Lecture Notes in Computer Science, 14349:144–154, Vancouver, Canada, October 8, 2023. https://doi.org/10.1007/978-3-031-45676-3_15

  16. Wen Z, Bao J, Yang S, Risacher SL, Saykin AJ, Thompson PM, Davatzikos C, Huang H, Zhao Y, Shen L. (2023) Identifying shared neuroanatomic architecture between cognitive traits through multiscale morphometric correlation analysis. MMMI’23: The 4th International Workshop on Multiscale Multimodal Medical Imaging, Lecture Notes in Computer Science, in press, Lecture Notes in Computer Science, Vancouver, Canada, October 8, 2023.

  17. Zhou R, Zhou H, Chen B, Shen L, Zhang Y, He L. (2023) Attentive deep canonical correlation analysis for diagnosing Alzheimer's disease using multimodal imaging genetics. MICCAI’23: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 14221:681–691, Vancouver, Canada, October 8-12, 2023. [32% acceptance rate] https://doi.org/10.1007/978-3-031-43895-0_64

  18. Wang Z, Zhan Q, Tong B, Yang S, Hou B, Huang H, Saykin A, Thompson PM, Davatzikos C, Shen L. (2023) Distance-weighted Sinkhorn loss: a novel approach for alzheimer's disease classification. ACM-BCB’23: The ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, under consideration for publication in a partner journal, Houston, TX, September 03-06, 2023. [10% acceptance rate for oral papers]

  19. Zhou Z, Tong B, Ataee Tarzanagh D, Hou B, Saykin A, Long Q, Shen L. (2023) Multi-group tensor canonical correlation analysis. ACM-BCB’23: The ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Article #12:1–10, Houston, TX, September 03-06, 2023. [10% acceptance rate for oral papers] [Best Paper Award] https://dl.acm.org/doi/10.1145/3584371.3612962

  20. Tarzanagh DA, Hou B, Tong B, Long Q, Shen L. (2023) Fairness-aware class imbalanced learning on multiple subgroups. UAI’23: The 39th Conference on Uncertainty in Artificial Intelligence, PMLR 216:2123-2133, July 31-August 4, 2023. https://proceedings.mlr.press/v216/tarzanagh23a.html [31% acceptance rate] 

  21. Wang X, Feng Y, Tong B, Bao J, Ritchie MD, Saykin AJ, Moore JH, Urbanowicz R, Shen L. (2023) Exploring automated machine learning for cognitive outcome prediction from multimodal brain imaging using STREAMLINE. AMIA-IS’23: AMIA Informatics Summit, pp 544-550, Seattle, WA, March 13-16, 2023. https://pubmed.ncbi.nlm.nih.gov/37350896/

  22. Tong B, Risacher SL, Bao J, Feng Y, Wang X, Ritchie MD, Saykin AJ, Moore JH, Urbanowicz R, Shen L. (2023) Comparing amyloid imaging normalization strategies for Alzheimer’s disease classification using an automated machine learning pipeline. AMIA-IS’23: AMIA Informatics Summit, pp 525-533, Seattle, WA, March 13-16, 2023. [Student Paper Award Finalist] https://pubmed.ncbi.nlm.nih.gov/37350880/

  23. Garai S, Xu F, Duong-Tran D, Zhao Y, Shen L. (2023) Mining correlation between fluid intelligence and whole-brain large scale structural connectivity. AMIA-IS’23: AMIA Informatics Summit, pp 225-233, Seattle, WA, March 13-16, 2023. https://pubmed.ncbi.nlm.nih.gov/37350917/

  24. Lee BN, Wang J, Nho K, Saykin AJ, Shen L. (2023) Discovering precision AD biomarkers with varying prognosis effects in genetics driven subpopulations. AMIA-IS’23: AMIA Informatics Summit, pp 340-349, Seattle, WA, March 13-16, 2023. [Marco Ramoni Distinguished Paper Award for Translational Bioinformatics] https://pubmed.ncbi.nlm.nih.gov/37350892/

  25. Wang Z, Chen J, Yang W, Garai S, Xu F, Wen J, Davatzikos C, Shen L. (2023) Shape analysis of amygdala atrophy using spharm-OT. SPIE MI’23: SPIE Medical Imaging – Image Processing, in press, San Diego, CA, USA, Feb 19-23, 2023. http://dx.doi.org/10.1117/12.2654399

  26. Pala D, Lee B, Ning X, Kim D, Shen L, for the ADNI. (2022) Mediation analysis and mixed-effects models for the identification of stage-specific imaging genetics patterns in Alzheimer's disease. BIBM’22 Artificial Intelligence Techniques for BioMedicine and HealthCare, pp 2667-2673, Las Vegas, NV, USA, Dec 6-8, 2022. https://pubmed.ncbi.nlm.nih.gov/36824222/

  27. Xu F, Garai S, Duong-Tran D, Saykin AJ, Zhao Y, Shen L, for the ADNI. (2022) Consistency of graph theoretical measurements of Alzheimer’s disease fiber density connectomes across multiple parcellation scales. BIBM’22: IEEE Int. Conf. on Bioinformatics and Biomedicine, pp 1323-1328, Las Vegas, NV, USA, Dec 6-8, 2022. [20% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/37041884/

  28. Wang Z, Yang W, Ryan K, Garai S, Auerbach BM, Shen L. (2022) Using optimal transport to improve spherical harmonic quantification of complex biological shapes. BIBM’22: IEEE Int. Conf. on Bioinformatics and Biomedicine, pp 1255-1261, Las Vegas, NV, USA, Dec 6-8, 2022. [20% acceptance rate] [BIBM 2022 Best Paper Award] https://doi.org/10.1109/BIBM55620.2022.9995036

  29. Sha J, Bao J, Liu K, Yang S, Wen Z, Cui Y, Wen J, Davatzikos C, Moore JH, Saykin AJ, Long Q, Shen L, for the ADNI. (2022) Preference matrix guided sparse canonical correlation analysis for genetic study of quantitative traits in Alzheimer's disease. BIBM’22: IEEE Int. Conf. on Bioinformatics and Biomedicine, pp 541-548, Las Vegas, NV, USA, Dec 6-8, 2022. [20% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/36845995/

  30. Jun Y, Zalatan B, Chen Y, Shen L, He L. (2022) Tensor-based multi-modal multi-target regression for Alzheimer’s disease prediction. BIBM’22: IEEE Int. Conf. on Bioinformatics and Biomedicine, pp 639-646, Las Vegas, NV, USA, Dec 6-8, 2022. [20% acceptance rate] 10.1109/BIBM55620.2022.9995078

  31. Machado-Reyes D, Kim M, Chao H, Hahn J, Shen L, Yan P. (2022) Genomics transformer for diagnosing Parkinson's disease. BHI’22: The IEEE International Conference on Biomedical and Health Informatics, pp. 01-04, doi: 10.1109/BHI56158.2022.9926815, Ioannina, Greece, September 27-30, 2022. https://pubmed.ncbi.nlm.nih.gov/36824448/

  32. Yu J, Kong Z, Zhan L, Shen L, He L. (2022) Tensor-based multi-modality feature selection and regression for Alzheimer’s disease diagnosis. BIOS’22: 8th International Conference on Bioinformatics and Biosciences, pp. 123-134, Vienna, Austria, October 29-30, 2022. https://pubmed.ncbi.nlm.nih.gov/36880061/

  33. Zhou H, Zhang Y, Chen B, Shen L, He L. (2022) Sparse interpretation of graph convolutional networks for multi-modal diagnosis of Alzheimer's disease. MICCAI’22: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 13438:469–478, Singapore, Sep 18-22, 2022. [31% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/36827208/

  34. Machado-Reyes D, Kim M, Chao H, Shen L, Yan P. (2022) Connectome transformer with anatomically inspired attention for Parkinson’s diagnosis. ACM-BCB’22: The ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Article No. 35, Chicago, August 7-10, 2022. https://doi.org/10.1145/3535508.3545544

  35. Zhou H, He L, Zhang Y, Shen L, Chen B. (2022) Interpretable graph convolutional network of multi-modality brain imaging for Alzheimer's disease diagnosis. ISBI’22: IEEE Int. Sym. on Biomedical Imaging, Hybrid Conference based on ITC Royal Bengal, Kolkata, India, March 28-31, 2022. https://doi.org/10.1109/ISBI52829.2022.9761449

  36. Bao J*, Wen Z*, Kim M, Zhao X, Lee BN, Jung SH, Davatzikos C, Saykin AJ, Thompson PM, Kim D, Zhao Y, Shen L, for the ADNI. (2022) Identifying highly heritable brain amyloid phenotypes through mining Alzheimer's imaging and sequencing biobank data. PSB'22: Pac Symp Biocomput. 2022;27:109-20. (* Equal Contribution) https://pubmed.ncbi.nlm.nih.gov/34890141/

  37. Bao J*, Wen Z*, Kim M, Saykin AJ, Thompson PM, Zhao Y, Shen L, for the ADNI. (2022) Identifying imaging genetic associations via regional morphometricity estimation. PSB'22: Pac Symp Biocomput. 2022;27:97-108. (* Equal Contribution) https://pubmed.ncbi.nlm.nih.gov/34890140/

  38. Kim M, Kim J, Qu J, Huang H, Sohn KA, Long Q, Kim D, Shen L. (2021) Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data. BIBM’21: IEEE Int. Conf. on Bioinformatics and Biomedicine, pp. 1381-1384, Virtual Conference, Dec 9-12, 2021. [20% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/35299717/

  39. Li H, Fang S, Goni J, Saykin AJ, Shen L. (2021) Interactive visualization of deep learning for 3D brain data analysis. IEEE ICCI*CC'21: 20th IEEE International Conference on Cognitive Informatics & Cognitive Computing, Online worldwide based on Banff AB, Canada, October 29-31, 2021. https://doi.org/10.1109/ICCICC53683.2021.9811312

  40. Zhao Y, Zhao X, Kim M, Bao J, Shen L. (2021) A novel Bayesian semi-parametric model for learning heritable imaging traits. MICCAI’21: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 12905:678-687, Virtual Conference, Sep 27-Oct 1, 2021. [33% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/35299630/

  41. Songdechakraiwut T, Shen L, Chung MK. (2021) Topological learning and its application to multimodal brain network integration. MICCAI’21: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 12902:166-176, Virtual Conference, Sep 27-Oct 1, 2021. [33% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/35098263/

  42. Eng Y*, Yao X*, Liu K, Risacher S, Saykin A, Long Q, Zhao Y, Shen L, for the ADNI. (2020) Polygenic mediation analysis of Alzheimer’s disease implicated intermediate amyloid imaging phenotypes. AMIA’20: AMIA 2020 Annual Symposium, pp 422-431, Virtual Conference, November 14-18, 2020. (* Equal Contribution) https://pubmed.ncbi.nlm.nih.gov/33936415/

  43. Feng Y, Kim M, Yao X, Liu K, Long Q, Shen L. (2020) Deep multiview learning for population subtyping with multimodal imaging. BIBE’20: IEEE Int. Conf. on BioInformatics and BioEngineering, pp 308-314, Virtual Conference, October 26-28, 2020 USA. https://pubmed.ncbi.nlm.nih.gov/33654579/

  44. Bao J, Kim M, Sun Q, Hara A, Maupome G, Shen L. (2020) Estimating hard-tissue conditions from dental images via machine learning. BIBE’20: IEEE Int. Conf. on BioInformatics and BioEngineering, pp 315-322, Virtual Conference, October 26-28, 2020 USA. https://doi.org/10.1109/BIBE50027.2020.00058

  45. Kim M, Bao J, Liu K, Park B, Park H, Shen L. (2020) Structural connectivity enriched functional brain network using simplex regression with GraphNet. MLMI’20: Machine Learning in Medical Imaging, Lecture Notes in Computer Science, 12436: 292-302, Virtual Conference, October 4, 2020. https://pubmed.ncbi.nlm.nih.gov/34766171/

  46. Li J, Bian C, Chen D, Meng X, Luo H, Liang H, Shen L. (2020) Persistent feature analysis of multimodal brain network using generalized fused lasso for EMCI identification. MICCAI’20: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 12267: 44-52, Virtual Conference, October 4-8, 2020. [31% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/34766172/

  47. Yang P, Yang Q, Wei Z, Shen L, Wang T, Lei B, Peng Z. (2020) Spatial similarity-aware learning and fused deep polynomial network for detection of obsessive-compulsive disorder. MICCAI’20: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 12267: 603-612, Virtual Conference, October 4-8, 2020. [31% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/34700244/

  48. Kim M, Won JH, Hong J, Kwon J, Park H, Shen L. (2020) Deep network-based feature selection for imaging genetics: Application to identifying biomarkers for Parkinson’s disease. ISBI’20: IEEE Int Sym on Biomedical Imaging, pp 1920-1923, April 3-7, 2020; Iowa City, IA. https://pubmed.ncbi.nlm.nih.gov/34594479/

  49. Brand L, Nichols K, Wang H, Huang H, Shen L, for the ADNI. (2020) Predicting longitudinal outcomes of Alzheimer's disease via a tensor-based joint classification and regression model. PSB’20: Pac Symp Biocomput., 25:7-18, Big Island of Hawaii, January 3-7, 2020. https://pubmed.ncbi.nlm.nih.gov/31797582/

  50. Du L, Fang L, Liu K, Yao X, Risacher S, Han J, Guo L, Saykin A, Shen L. (2019) A dirty multi-task learning method for multi-modal brain imaging genetics. MICCAI’19: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 11767:447-455, Shenzhen, China, October 13-17, 2019. [31% acceptance rate] https://doi.org/10.1007/978-3-030-32251-9_49

  51. Lu L, Elbeleidy S, Backer L, Wang H, Huang H, Shen L. (2019) Improved prediction of cognitive outcomes via globally aligned imaging biomarker enrichments over progressions. MICCAI’19: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 11767:140-148, Shenzhen, China, October 13-17, 2019. [25% early acceptance rate] https://pubmed.ncbi.nlm.nih.gov/33819146/ 

  52. Chung MK, Xie L, Huang S, Wang Y, Yan J, Shen L. (2019) Rapid acceleration of the permutation test via transpositions. CNI’19: MICCAI Workshop on Connectomics in Neuroimaging, Lecture Notes in Computer Science, 11848:42-53, Shenzhen, China, October 13, 2019. https://pubmed.ncbi.nlm.nih.gov/34514470/

  53. Peng B, Ren Z, Yao X, Liu K, Saykin AJ, Shen L, Ning X, for the ADNI. (2019) Prioritizing amyloid imaging biomarkers in Alzheimer's disease via learning to rank. MBIA’19: MICCAI Workshop on Multimodal Brain Image Analysis, Lecture Notes in Computer Science, 11846:139-148, Shenzhen, China, October 17, 2019. https://doi.org/10.1007/978-3-030-33226-6_16

  54. Mussabaeva A, Pisov M, Kurmukov A, Kroshnin A, Shen L, Cong S, Wang L, and Gutman B. (2019) Diffeomorphic metric learning and template optimization for registration-based predictive models. MFCA’19: MICCAI Workshop on Mathematical Foundations of Computational Anatomy, Lecture Notes in Computer Science, 11846:151-161, Shenzhen, China, October 17, 2019. https://doi.org/10.1007/978-3-030-33226-6_17

  55. Yao X, Cong S, Yan J, Risacher SL, Saykin AJ, Moore JH, Shen L. (2019) Mining regional imaging genetic associations via voxel-wise enrichment analysis. BHI’19: IEEE International Conference on Biomedical and Health Informatics, Chicago, IL, May 19-22, 2019. [31% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/31742256/

  56. Peng B, Yao X, Risacher SL, Saykin AJ, Shen L, Ning X. (2019) Prioritization of cognitive assessments in Alzheimer's disease via learning to rank using brain morphometric data. BHI’19: IEEE International Conference on Biomedical and Health Informatics, Chicago, IL, May 19-22, 2019. [11% acceptance rate for oral presentations] https://pubmed.ncbi.nlm.nih.gov/31934686/

  57. Du L, Liu K, Yao X, Risacher SL, Guo L, Saykin AJ, Shen L. (2019) Diagnosis status guided brain imaging genetics via integrated regression and sparse canonical correlation analysis. ISBI’19: IEEE Int. Sym. on Biomedical Imaging, Venice, Italy, April 8-11, 2019. https://pubmed.ncbi.nlm.nih.gov/31844486/

  58. Chung MK, Huang SG, Gritsenko A, Shen L, Lee H (2019) Statistical inference on the number of cycles in brain networks. ISBI’19: IEEE Int. Sym. on Biomedical Imaging, Venice, Italy, April 8-11, 2019. https://pubmed.ncbi.nlm.nih.gov/31687091/

  59. Du L, Liu K, Yao X, Risacher SL, Han J, Guo L, Saykin AJ, Shen L. (2018) Fast multi-task SCCA learning with feature selection for multi-modal brain imaging genetics. BIBM’18: IEEE Int. Conf. on Bioinformatics and Biomedicine. Madrid, Spain, December 3-6, 2018. [19.6% acceptance rate] [Best Paper Award] https://pubmed.ncbi.nlm.nih.gov/30881731/

  60. Liu K, Shen L, Jiang H. (2018) A unified model for robust differential expression analysis of RNA-seq data. BIBM’18: IEEE Int. Conf. on Bioinformatics and Biomedicine. Madrid, Spain, December 3-6, 2018. [19.6% acceptance rate] https://doi.org/10.1109/BIBM.2018.8621331

  61. Li H, Fang S, Mukhopadhyay S, Saykin A, Shen L. (2018) Interactive machine learning by visualization: A small data solution. HMData’18: The 2nd IEEE Workshop on Human-in-the-loop Methods and Human Machine Collaboration in BigData. Seattle, WA, December 10, 2018. https://pubmed.ncbi.nlm.nih.gov/31061990/

  62. Brand L, Wang H, Huang H, Risacher S, Saykin A, Shen L, for the ADNI. (2018) Joint high-order multi-task feature learning to predict the progression of Alzheimer's disease. MICCAI’18: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 11070:555-562, Granada, Spain, September 16-20, 2018. [~33% acceptance rate] https://pubmed.ncbi.nlm.nih.gov/31179446/

  63. Xie L, Amico E, Salama P, Wu Y, Fang S, Sporns O, Saykin A, Goni J, Yan J, Shen L. (2018) Heritability estimation of reliable connectomic features. CNI’17: MICCAI Workshop on Connectomics in NeuroImaging, Lecture Notes in Computer Science, 11083:58-66, Granada, Spain, September 20, 2018. https://pubmed.ncbi.nlm.nih.gov/30906933/

  64. Musabaeva A, Kroshnin A, Kurmukov A, Dodonova Y, Shen L, Cong S, Wang L, Gutman BA. (2018) Image registration and predictive modeling: Learning the metric on the space of diffeomorphisms. ShapeMI’17: MICCAI Workshop on Shape in Medical Imaging, Lecture Notes in Computer Science, in press, Granada, Spain, September 20, 2018. https://doi.org/10.1007/978-3-030-04747-4_15

  65. Yan J, Raja V, Huang Z, Enrico A, Nho K, Fang S, Sporns O, Wu Y, Saykin AJ, Goni J, Shen L. (2018) Brain-wide structural connectivity alterations under the control of Alzheimer risk genes. ICIBM’18: Int. Conf. on Intelligent Biology and Medicine, Los Angeles, CA, USA, June 10-12, 2018. https://pubmed.ncbi.nlm.nih.gov/32095160/ 

  66. Chiang W, Shen L, Li L, Ning X. (2018) Drug-drug interaction prediction based on co-medication patterns and graph matching. ICIBM’18: Int. Conf. on Intelligent Biology and Medicine, Los Angeles, CA, USA, June 10-12, 2018. https://dx.doi.org/10.1504/IJCBDD.2020.105093 

  67. Liu K, Wang H, Risacher S, Saykin AJ, Shen L, for the ADNI. (2018) Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer’s disease analysis. ISBI’18:  IEEE Int. Sym. on Biomedical Imaging, pages 1402-1405, Washington DC, April 4-7, 2018. https://doi.org/10.1109/ISBI.2018.8363834

  68. Lu L, Wang H, Yao X, Risacher S, Saykin AJ, Shen L, for the ADNI. (2018) Predicting progressions of cognitive outcomes via high-order multi-modal multi-task feature learning. ISBI’18:  IEEE Int. Sym. on Biomedical Imaging, pages 545-548, Washington DC, April 4-7, 2018. https://doi.org/10.1109/ISBI.2018.8363635

  69. Yan J, Liu K, Li H, Amico E, Risacher SL, Wu Y, Fang S, Sporns O, Saykin AJ, Goni J, Shen L. (2018) Joint exploration and mining of memory-relevant brain anatomic and connectomic patterns via a three-way association model. ISBI’18:  IEEE Int. Sym. on Biomedical Imaging, pages 6-9, Washington DC, April 4-7, 2018. https://pubmed.ncbi.nlm.nih.gov/30271529/

  70. Li H, Fang S, Zigon B, Sporns O, Saykin AJ, Goñi J, Shen L. (2017) BECA: A Software Tool for Integrated Visualization of Human Brain Data. In: Zeng Y, He Y, Kotaleski JH, Martone M, Xu B, Peng H, et al., editors. BI'17: Brain Informatics: International Conference, Beijing, China, November 16-18, 2017, Proceedings. Cham: Springer International Publishing; 2017. p. 285-91. https://doi.org/10.1007/978-3-319-70772-3_27

  71. Liu K, Yao X, Yan J, Chasioti D, Risacher S, Nho K, Saykin A, Shen L, for the ADNI. (2017) Transcriptome-guided imaging genetic analysis via a novel sparse CCA algorithm. MICGen 2017: MICCAI Workshop on Imaging Genetics, Lecture Notes in Computer Science, 10551:220-229, Quebec City, Canada, September 10, 2017. 

  72. Huang Y, Du L, Liu K, Yao X, Risacher SL, Guo L, Saykin AJ, Shen L, for the ADNI. (2017) A fast SCCA algorithm for big data analysis in brain imaging genetics. MICGen 2017: MICCAI Workshop on Imaging Genetics, Lecture Notes in Computer Science, 10551:210-219, Quebec City, Canada, September 10, 2017.  

  73. Ning X, Shen L, Li L. (2017) Predicting high-order directional drug-drug interaction relations. ICHI’17: The 5th IEEE International Conference on Healthcare Informatics, 6 pages, Park City, Utah, August 23-26, 2017.

  74. Ning X, Schleyer T, Shen L, Li L. (2017) Pattern discovery from directional high-order drug-drug interaction relations. ICHI’17: The 5th IEEE International Conference on Healthcare Informatics, 9 pages, Park City, Utah, August 23-26, 2017. 

  75. Du L, Zhang T, Liu K, Yan J, Yao X, Risacher SL, Saykin AJ, Han J, Guo L, Shen L. (2017) Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach. IPMI’17: Information Processing in Medical Imaging, Lecture Notes in Computer Science, 10265:543-555, Boone, North Carolina, USA, June 25-30, 2017. [33% acceptance rate]

  76. Wang X, Liu K, Yan J, Risacher SL, Saykin AJ, Shen L, Huang H. (2017) Predicting interrelated Alzheimer's disease outcomes via new self-learned structured low-rank model. IPMI’17: Information Processing in Medical Imaging, Lecture Notes in Computer Science, 10265:198-209, Boone, North Carolina, June 25-30, 2017. [33% acceptance rate]

  77. Wang X, Yan J, Yao X, Kim S, Nho K, Risacher SL, Saykin AJ, Shen L, Huang H, for the ADNI. (2017) Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model. RECOMB'17: The 21st Annual International Conference on Research in Computational Molecular Biology, pp 287-302, Hong Kong, May 3-7, 2017. [21% acceptance rate]

  78. Yao X, Yan J, Risacher S, Moore J, Saykin A, Shen L. (2017) Network-based genome wide study of hippocampal imaging phenotype in Alzheimer’s disease to identify functional interaction modules. ICASSP'17: The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing, 10.1109/ICASSP.2017.7953342, New Orleans, March 5-9, 2017.

  79. Shen L, Cooper L (2017) Imaging Genomics (Session Introduction). PSB’17: Pac Symp Biocomput., 22:51-57, Big Island of Hawaii, January 3-7, 2017.

  80. Yan J, Risacher SL, Nho K, Saykin AJ, Shen L. (2017) Identification of discriminative proteomics associations in Alzheimer’s disease via a novel sparse correlation model. PSB’17: Pac Symp Biocomput., 22:94-104, Big Island of Hawaii, January 3-7, 2017.

  81. Du L, Zhang T, Liu K, Yao X, Yan J, Risacher SL, Guo L, Saykin AJ, Shen L. (2017) Sparse Canonical Correlation Analysis via Truncated l1-norm with Application to Brain Imaging Genetics.  BIBM’16: IEEE Int. Conf. on Bioinformatics and Biomedicine. Shenzhen, China; 2016.

  82. Luo D, Huo Z, Wang Y, Saykin AJ, Shen L, Huang H. (2016) New probabilistic multi-graph decomposition model to identify consistent human brain network modules. ICDM’16: IEEE International Conference on Data Mining, Barcelona, December 13-15, 2016. In press.

  83. Wen Q, Stirling BD, Sha L, Shen L, Whalen PJ, Wu Y. (2016) Parcellation of human amygdala subfields using orientation distribution function and spectral k-means clustering. CDMRI'16: MICCAI 2016 Workshop on Computational Diffusion MRI, Mathematics and Visualization, Athens, Greece, October 21, 2016.

  84. Cong S, Rizkalla M, Salama P, Risacher SL, West JD, Wu Y, Apostolova L, Tallman E, Saykin AJ, Shen L, ADNI (2016) Building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans using landmark-free surface registration. MWSCAS'16: The IEEE 59th International Midwest Symposium on Circuits and Systems, Abu Dhabi, United Arab Emirates, October 16-19, 2016.

  85. Inlow M, Cong S, Risacher SL, West JD, Rizkalla M, Salama P, Saykin AJ, Shen L, for the ADNI. (2016) A new statistical image analysis approach and its application to hippocampal morphometry. MIAR’16: Medical Imaging and Augmented Reality, Lecture Notes in Computer Science, 9805:302-310, Bern, Switzerland, August 24-26, 2016.

  86. Wang J, Fang S, Li H, Goñi J, Saykin AJ, Shen L. Multigraph visualization for feature classification of brain network data. (2016) In: Sedlmair NAaM, editor. EuroVA’16: EuroVis Workshop on Visual Analytics. Groningen, the Netherlands: The Eurographics Association; 2016. p. 61-65.

  87. Hao X, Yan J, Yao X, Risacher SL, Saykin AJ, Zhang D, Shen L. (2016) Diagnosis-guided method for identifying multi-modality neuroimaging biomarkers associated with genetic risk factors in Alzheimer's disease. PSB'16: Pac Symp Biocomput. 21:108-119, 2016.

  88. Yan J, Du L, Kim S, Risacher SL, Huang H, Inlow M, Moore JH, Saykin AJ, Shen L, for the ADNI. (2015) BoSCCA: Mining stable imaging and genetic associations with implicit structure learning. MICGen'15: MICCAI Workshop on Imaging Genetics, October 9, 2015. 

  89. Liang H, Meng X, Chen F, Zhang Q, Yan J, Yao X, Kim S, Wang L, Feng W, Saykin AJ, Li J, Shen L, and for the ADNI. (2015) A network-based framework for mining high-level imaging genetic associations. MICGen'15: MICCAI Workshop on Imaging Genetics, October 9, 2015. 

  90. Li H, Fang S, Goni J, Contreras JA, Liang Y, Cai C, West JD, Risacher SL, Wang Y, Sporns O, Saykin AJ, Shen L†§, for the ADNI. (2015) Integrated visualization of human brain connectome data. BIH'15: Special Session on Neuroimaging Data Analysis and Applications, Lecture Notes in Artificial Intelligence, 9250:295-305, London, UK, 30 August - 2 September 2015.

  91. Du L, Yan J, Kim S, Risacher SL, Huang H, Inlow M, Moore JH, Saykin AJ, Shen L, for the ADNI. (2015) GN-SCCA: GraphNet sparse canonical correlation analysis for brain imaging genetics. BIH'15: Special Session on Neuroimaging Data Analysis and Applications, Lecture Notes in Artificial Intelligence, 9250: 275-284, London, UK, 30 August - 2 September 2015.

  92. Yao X, Yan J, Kim S, Nho K, Risacher SL, Inlow M, Moore JH, Saykin AJ, Shen L, for the Alzheimer's Disease Neuroimaging Initiative. (2015) Two-dimensional enrichment analysis for mining high-level imaging genetic associations. BIH'15: International Conference on Brain Informatics & Health, Lecture Notes in Artificial Intelligence, 9250:115-124, London, UK, 30 August - 2 September 2015. [50% acceptance rate]

  93. Gao H, Cai C, Yan J, Goni Cortes J, Wang Y, Nie F, West J, Saykin AJ, Shen L, Huang H (2015) Identifying connectome module patterns via new balanced multi-graph normalized cut. MICCAI’15: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 9350:169-76, Munich, Germany, October 5-9, 2015. [~30% acceptance rate]

  94. Cong S, Rizkalla M, Salama P, West JD, Risacher SL, Saykin AJ, Shen L, ADNI (2015) Surface-based morphometric analysis of hippocampal subfields in mild cognitive impairment and Alzheimer's disease. MWSCAS'15: The IEEE 58th International Midwest Symposium on Circuits and Systems, pp 813-816, Fort Collins, Colorado, USA, August 2-5, 2015.

  95. Liang Y, Fang S, Brandstatter T, Cai C, Wang Y, West JD, Goni Cortes J, Saykin AJ, Shen L (2014) Brain connectome visualization for feature classification. IEEE VIS 2014, poster paper, Paris, France, November 9-14, 2014.

  96. Wang D, Wang Y, Nie F, Yan J, Saykin AJ, Shen L, Huang H (2014) Human connectome module pattern detection using a new multi-graph minmax cut model. MICCAI’14: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 8675:313-320, Boston, MA, September 14-18, 2014. [~30% acceptance rate]

  97. Du L*, Yan J*, Kim S, Risacher SL, Huang H, Inlow M, Moore JH, Saykin AJ, Shen L, for the ADNI (2014) A novel structure-aware sparse learning algorithm for brain imaging genetics. MICCAI’14: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 8675:329-336, Boston, MA, September 14-18, 2014. (*equal contribution) [~30% acceptance rate]

  98. Cong S, Rizkalla M, Du EY, West JD, Risacher SL, Saykin AJ, Shen L, ADNI (2014) Building a surface atlas of hippocampal subfields from MRI scans using FreeSurfer, FIRST and SPHARM. MWSCAS'14: The IEEE 57th International Midwest Symposium on Circuits and Systems, pp 813-816, College Station, Texas, USA, August 3-6, 2014.

  99. Yan J, Zhang H, Du L, Wernert E, Saykin AJ and Shen L (2014) Accelerating sparse canonical correlation analysis for large brain imaging genetics data. XSEDE'14: The Annual Extreme Science and Engineering Discovery Environment Conference, Atlanta, GA, July 13-18, 2014.

  100. Sheng J, Kim S, Yan J, Moore JH, Saykin AJ, Shen L, for the ADNI (2014) Data synthesis and method evaluation for brain imaging genetics. ISBI’14:  IEEE Int. Sym. on Biomedical Imaging, pp 1202-1205, Beijing, China, 28 April - 2 May, 2014.

  101. Yan J, Huang H, Kim S, Moore JH, Saykin AJ, Shen L, for the ADNI (2014) Joint identification of imaging and proteomics biomarkers of Alzheimer’s disease using network-guided sparse learning. ISBI’14:  IEEE Int. Sym. on Biomedical Imaging, pp 665-668, Beijing, China, 28 April - 2 May, 2014.

  102. Pan Q, Hu T, Shen L, Saykin AJ, Moore JH (2013) Topological analysis of statistical epistasis networks reveals pathways associated with Alzheimer’s disease. TBC'13: The 3rd Annual Translational Bioinformatics Conference, Seoul, Korea, Oct. 2th-4th, 2013.

  103. Yan J, Huang H, Risacher SL, Kim S, Inlow M, Moore JH, Saykin AJ, Shen L, for the ADNI (2013) Network-guided sparse learning for predicting cognitive outcomes from MRI measures. MBIA’13: MICCAI Workshop on Multimodal Brain Image Analysis, Lecture Notes in Computer Science, 8159:202-210, Nagoya, Japan, September 22, 2013.

  104. Kim D, Kim S, Risacher SL, Shen L, Ritchie MD, Weiner MW, Saykin AJ, Nho K, for the ADNI (2013) A graph-based integration of multimodal brain imaging data for the prediction of early mild cognitive impairment (E-MCI). MBIA’13: MICCAI Workshop on Multimodal Brain Image Analysis, Lecture Notes in Computer Science, 8159:159-169, Nagoya, Japan, September 22, 2013.

  105. Kim S, Nho K, Risacher SL, Inlow M, Swaminathan S, Yoder KK, Shen L, West JD, McDonald BC, Tallman EF, Hutchins GD, Fletcher JW, Farlow MR, Ghetti B, Saykin AJ (2013) PARP1 gene variation and microglial activity on [11C]PBR28 PET in older adults at risk for Alzheimer's disease. MBIA’13: MICCAI Workshop on Multimodal Brain Image Analysis, Lecture Notes in Computer Science, 8159:150-158, Nagoya, Japan, September 22, 2013.

  106. Hibar DP, Medland SE, Stein JL, Kim S, Shen L, Saykin AJ, de Zubicaray GI,  McMahon KL, Montgomery GW, Martin NG, Wright MJ, Djurovic S, Agartz I, Andreassen OA, Thompson PM (2013) Genetic clustering on the hippocampal surface for genome-wide association studies. MICCAI’13: Med Image Comput Comput Assist Interv, LNCS, in press, Nagoya, Japan, September 22-26, 2013. [33% acceptance rate]

  107. Huang H, Yan J, Nie F, Huang J, Cai W, Saykin AJ, Shen L (2013) A new sparse simplex model for brain anatomical and genetic network analysis. MICCAI’13: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 8150:625-632, Nagoya, Japan, September 22-26, 2013. [33% acceptance rate]

  108. Wang D, Nie F, Huang H, Yan J, Risacher SL, Saykin AJ, Shen L, for the ADNI (2013) Structural brain network constrained neuroimaging marker identification for predicting cognitive functions. IPMI'13: Information Processing in Medical Imaging, Lecture Notes in Computer Science, 7917:536-547, Asilomar, CA, 6/29-7/3, 2013. [32% acceptance rate]

  109. Wang H, Nie F, Huang H, Yan J, Kim S, Risacher SL, Saykin AJ, Shen L, ADNI (2012) High-order multi-task feature learning to identify longitudinal phenotypic markers for Alzheimer disease progression prediction. NIPS’12: Neural Information Processing Systems, Lake Tahoe, Nevada, December 3-6, 2012. [1.36% acceptance rate for full oral presentation, 20/1467]

  110. Yan J, Li T, Wang H, Huang H, Wan J, Nho K, Kim S, Risacher SL, Saykin AJ, Shen L, for the ADNI (2012) Identification of novel cortical surface biomarkers for predicting cognitive outcomes based on group-level L-21 norm. MICCAI'12 Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders, pp 199-208, Nice, France, October 5, 2012.

  111. Li T, Wan J, Zhang Z, Yan J, Kim S, Risacher SL, Fang S, Beg MF, Wang L, Saykin AJ, Shen L, for the ADNI (2012) Hippocampus as a predictor of cognitive performance: Comparative evaluation of analytical methods and morphometric measures. MICCAI'12 Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders, pp 125-136, Nice, France, October 5, 2012.

  112. Yan J, Risacher SL, Kim S, Simon JC, Li T, Wan J, Wang H, Huang H, Saykin AJ, Shen L, for the ADNI (2012) Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. MBIA’12: MICCAI Workshop on Multimodal Brain Image Analysis, Lecture Notes in Computer Science, 7509:1-17, Nice, France, October 1, 2012. 

  113. Wan J, Zhang Z, Yan J, Li T, Rao B, Fang S, Kim S, Risacher S, Saykin A, Shen L, for the ADNI (2012) Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer’s disease. CVPR’12: IEEE Int. Conf. on Computer Vision and Pattern Recognition, 940-947, Providence, Rhode Island, June 18-20, 2012. [24% acceptance rate]

  114. Guo Y, Wang Y, Fang S, Chao H, Saykin AJ, Shen L (2012) Pattern visualization of human connectome data. EuroVis'12: Eurographics/IEEE Int Visualization Symp, pp 79-83, Vienna, Austria, June 5-8, 2012. [42% acceptance rate]

  115. Wang H, Nie F, Huang H, Risacher SL, Saykin AJ, Shen L (2011) Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. ICCV’11: IEEE Int. Conf. on Computer Vision, pp 557-562, Barcelona, Nov 6-13, 2011. [24% acceptance rate]

  116. Nho K, Shen L, Kim S, Risacher SL, Swaminathan S, Saykin AJ (2011) The effect of reference datasets and software tools on genotype imputation. AMIA'11: American Medical Informatics Association (AMIA) Annual Symp Proc, pp 1013-1018, Washington, DC, October 22-26, 2011. 

  117. Wang H, Nie F, Huang H, Risacher SL, Saykin AJ, Shen L, ADNI (2011) Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. MICCAI’11: Med Image Comput Comput Assist Interv, LNCS 6893:115-123, Toronto, Canada, September 18-22, 2011. [4.15% acceptance rate for oral papers, 34/819]

  118. Wan J, Kim S, Inlow M, Nho K, Swaminathan S, Risacher SL, Fang S, Weiner M, Beg F, Wang L, Saykin AJ, Shen L, ADNI (2011) Hippocampal surface mapping of genetic risk factors in AD via sparse learning models. MICCAI’11: Med Image Comput Comput Assist Interv, LNCS 6892:376-383, Toronto, Canada, September 18-22, 2011. [30% acceptance rate]

  119. Shen L, Kim S, Qi Y, Inlow M, Swaminathan S, Nho K, Wan J, Risacher S, Shaw L, Trojanowski J, Weiner M, Saykin A, ADNI (2011) Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. MBIA’11: MICCAI Workshop on Multimodal Brain Image Analysis, LNCS 7012:27-34, Toronto, Canada, September 18, 2011.

  120. Nho K, Shen L, Kim S, Risacher SL, West JD, Foroud T, Jack CR, Weiner MW, Saykin AJ, ADNI (2010) Automatic prediction of MCI conversion to probable AD using baseline structural MRI in the ADNI cohort. AMIA'10: American Medical Informatics Association (AMIA) Annual Symp Proc, pp 542-6, Washington, DC, Nov. 13-17, 2010. 

  121. Shen L, Qi Y, Kim S, Nho K, Wan J, Risacher SL, Saykin AJ, ADNI (2010) Sparse Bayesian learning for identifying imaging biomarkers in AD prediction. MICCAI’10: Med Image Comput Comput Assist Interv, LNCS 6363, pp 611-618, Beijing, China, 2010. [32% acceptance rate]

  122. Wan J, Shen L, Fang S, McLaughlin J, Autti-Ramo I, Fagerlund A, Riley E, Hoyme HE, Moore ES, Foroud T, CIFASD (2010) A framework for 3D analysis of facial morphology in fetal alcohol syndrome. MIAR’10: Medical Imaging and Augmented Reality, LNCS 6326:118-127, Beijing, China,  September 19-20, 2010. [29% acceptance rate for oral papers] 

  123. Wan J, Shen L, Sheehan KE, Kim S, Roth RM, Ford J, Fang S, Saykin AJ, Wishart HA (2009) Shape analysis of thalamic atrophy in multiple sclerosis. MICCAI'09 Workshop on Medical Image Analysis on Multiple Sclerosis, pp 93-104, Imperial College London, UK, September 20, 2009. 

  124. Kim S, Shen L, Saykin AJ, West JD (2009) Data synthesis and tool development for exploring imaging genomic patterns. CIBCB'09: IEEE Symp on Computational Intelligence in Bioinformatics and Computational Biology, pp 298-305, Nashville, TN, 3/30-4/2, 2009.

  125. Kim S, Shen L, Saykin AJ, West JD (2009) Visual exploration of genetic association with voxel-based imaging phenotypes in an MCI/AD study. EMBC'09: Conf Proc IEEE Eng Med Biol Soc., pp 3849-3852, Minneapolis, Minnesota, September 2-6, 2009.

  126. Li H, Yan M, Henschel R, Shen L (2009) Implementing 3D SPHARM surfaces registration on cell processor. SAAHPC'09: Symp on Application Accelerators in High Performance Computing, 3 pages, Urbana, Illinois, July 28-30, 2009.

  127. Shen L, Firpi HA, Saykin AJ, West JW (2008) Parametric surface modeling and registration for comparison of manual and automated segmentation of the hippocampus. MICCAI’08 Workshop on the Computational Anatomy and Physiology of the Hippocampus, 12 pages, NYC, Sep 6, 2008.

  128. Shen L, Saykin AJ, Chung MK, Huang H (2007) Morphometric analysis of hippocampal shape in mild cognitive impairment: an imaging genetics study. BIBE’07: IEEE 7th Int Symp on Bioinfo & BioEngi, pp 211-217, Boston, MA, October 14-17, 2007. [12% acceptance rate]

  129. Shen L, Huang H, Makedon FS, Saykin AJ (2007) Efficient registration of 3D SPHARM surfaces. CRV 2007: Fourth Canadian Conf on Computer and Robot Vision, pp 81-88, Montreal, QC, May 28-30, 2007. [24% acceptance rate for oral papers]

  130. Xu Y, Ford JC, Makedon FS, Popa D, Huang H, Shen L (2007) In-home localization for home care of Alzheimer's disease patients using wireless sensor networks. PTSADRD'07: Int Workshop on Pervasive Technologies for the Support of Alzheimer's Disease and Related Disorders Sufferers, 6 pages, Thessaloniki, February 24, 2007.

  131. Huang H, Shen L. Surface harmonics for shape modeling (2007) ICIP’07: 14th IEEE Int Conf on Image Processing, 2:553-556, San Antonio, Texas, September 16-19, 2007.

  132. Shen L, Saykin AJ, Chung MK, Huang H, Ford JC, Makedon FS, McHugh TL, Rhodes CH (2006) Morphometric analysis of genetic variation in hippocampal shape in mild cognitive impairment: Role of an IL-6 promoter polymorphism. CSB’06: LSS Computational Systems Bioinformatics Conf, 6 pages, Stanford University, CA, August 14-18, 2006.

  133. Chung MK, Shen L, Dalton KM, Davidson RJ (2006) Multi-scale voxel-based morphometry via weighted spherical harmonic representation. MIAR’06: Medical Imaging and Augmented Reality, LNCS 4091: 36-43, Shanghai, China, August 17-18, 2006.

  134. Shen L and Chung MK (2006) Large-scale modeling of parametric surfaces using spherical harmonics. 3DPVT’06: Int Symp on 3D Data Processing, Visualization & Transmission, 8 pages, UNC-CH, June 14-16, 2006.

  135. Huang H, Zhang L, Samaras D, Shen L, Makedon FS, Pearlman JD (2006) Hemispherical harmonic surface description and applications to medical image analysis. 3DPVT’06: Int Symp on 3D Data Processing, Visualization & Transmission, 8 pages, UNC-CH, June 14-16, 2006.

  136. Huang H, Shen L, Makedon FS, Pearlman JD (2006) A Spatio-temporal modeling method for shape representation. 3DPVT’06: Int Symp on 3D Data Processing, Visualization & Transmission, 7 pages, UNC-CH, June 14-16, 2006.

  137. Moolani V, Balasubramanian R, Shen L, Tandon A (2006) Shape analysis and spatio-temporal tracking of mesoscale eddies in Miami isopycnic coordinate ocean model. 3DPVT’06: Int Symp on 3D Data Processing, Visualization & Transmission, 8 pages, UNC-CH, June 14-16, 2006.

  138. Shen L, Huang H, Ford JC, Lu C, Gao L, Zheng W, Makedon FS, Pearlman JD (2006) Spatio-temporal analysis tool for modeling pulmonary nodules in MR images.  SPIE-MI'06: SPIE Medical Imaging, 6141:740-749, San Diego, CA, February 2006. 

  139. Huang H, Shen L, Zhang R, Makedon FS, Hettleman B, Pearlman JD (2006) Fast surface alignment for cardiac spatiotemporal modeling. SPIE-MI'06: SPIE Medical Imaging, 6144:1002-1009, San Diego, CA, February 2006.

  140. Zheng W, Wang Z, Shen L, Makedon FS, Pearlman JD (2006) Measuring blood delivery to solitary pulmonary nodule by perfusion magnetic resonance imaging. SPIE-MI'06: SPIE Medical Imaging, 6143:614332, 9 pages, San Diego, CA, February 2006.

  141. Huang H, Shen L, Zhang R, Makedon FS, Hettleman B, Pearlman JD (2005) Surface alignment of 3D spherical harmonic models: Application to cardiac MRI analysis. MICCAI’05: Med Image Comput Comput Assist Interv, LNCS 3749:67-74, Springer, Heidelberg, 2005. 

  142. Huang H, Shen L, Zhang R, Makedon FS, Pearlman JD (2005) A prediction framework for cardiac resynchronization therapy via 4D cardiac motion analysis. MICCAI’05: Med Image Comput Comput Assist Interv, LNCS 3749:704-711, Springer, Heidelberg, 2005.

  143. Huang H, Shen L, Zhang R, Makedon FS, Hettleman B, Pearlman JD (2005) Clustering based cardiac resynchronization therapy prediction using open source toolkit PRTools. ISC/NA-MIC Open-Source Workshop at MICCAI’05: Med Image Comput Comput Assist Interv, Palm Springs, California, USA, Oct 26-29, 2005.

  144. Huang H, Shen L, Makedon FS, Sheng Zhang, Greenberg M, Gao L, Pearlman JD (2005) A clustering-based approach for prediction of cardiac resynchronization therapy. ACM-AC'05: 20th ACM Symp on Applied Computing, pp 260-266, Santa Fe, NM, March 13-17, 2005.

  145. Shen L, Gao L, Zhuang Z, DeMuinck E, Huang H, Makedon FS, Pearlman JD (2005) An interactive 3D visualization and manipulation tool for effective assessment of angiogenesis and arteriogenesis using computed tomographic angiography. SPIE-MI'05: SPIE Medical Imaging, 5744: 848-858, San Diego, CA, February 12-17, 2005.

  146. Huang H, Shen L, Ford JC, Makedon FS, Zhang R, Gao L, Pearlman JD (2005) Functional analysis of cardiac MR images using SPHARM modeling. SPIE-MI'05: SPIE Medical Imaging, 5747:1384-1391, San Diego, CA, February 12-17, 2005.

  147. Shen L, Zheng W, Gao L, Huang H, Makedon FS, Pearlman JD (2005) Modeling time-intensity profiles for pulmonary nodules in MR images. EMBC’05: Conf Proc IEEE Eng Med Biol Soc, pp 1359-1362, Shanghai, China, September 1-4, 2005.

  148. Huang H, Zhang R, Fei Xiong, Makedon FS, Shen L, Hettleman B, Pearlman JD (2005) K-means+ method for improving gene selection for classification of microarray Data. CSB’05: IEEE Computational Systems Bioinformatics Conf, pp 110-111, Stanford, California, Aug 8-11, 2005.

  149. Shen L, Saykin AJ, McHugh TL, West JD, Rabin LA, Wishart HA, Chung MK, Makedon FS (2005) Morphometric analysis of 3D surfaces: Application to hippocampal shape in mild cognitive impairment. CVPRIP’05: 6th Int. Conf. on Computer Vision, Pattern Recognition and Image Processing, pp 699-702, Salt Lake City, Utah, July 21-26, 2005.

  150. Wang Y, Makedon FS, Ford JC, Shen L, Goldin D (2004) Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram. JCDL'04: The 4th ACM/IEEE-CS Joint Conf. on Digital Libraries, pp 202-211, Tucson, Arizona, June 7-11, 2004.

  151. Steinberg T, Ford JC, Ouyang Y, Shen L, Wang Y, Zheng W, Makedon FS (2004) Tracking resource usage using heterogeneous feature spaces with local exceptions. EDBT'04 Workshop: Clustering Information over the Web, Heraklion-Crete, Greece, March 14th, 2004.

  152. Shen L and Makedon FS (2004) Spherical parameterization for 3D surface analysis in volumetric images. ITCC'04: IEEE Int Conf Info Tech, pp 643-649, Las Vegas, NV, April 5-7, 2004.

  153. Makedon FS, Ye S, Sheng Zhang, Ford JC, Shen L, Kapidakis S (2004) Data brokers: Building collections through automated negotiation. SETN'04: The 3rd Hellenic Conf on Artificial Intelligence, LNCS 3025, pp 13-22, Samos, Greece, May 5-8, 2004.

  154. Shen L, Makedon FS, Saykin AJ (2004) Shape-based discriminative analysis of combined bilateral hippocampi using multiple object alignment. SPIE-MI'04: SPIE Medical Imaging, 5370: 274-282, San Diego, CA, February 14-19, 2004.

  155. Huang H, Makedon FS, Pearlman JD, Ford JC, Shen L, Wang Y, Gao L (2004) Efficient similarity retrieval framework for temporal shape sequences: A case study in cardiac MR images. EMBC’ 04: Conf Proc IEEE Eng Med Biol Soc, pp 3250-3253, San Francisco, California, September 1, 2004.

  156. Shen L, Ford JC, Makedon FS, Wang Y, Steinberg T, Ye S, Saykin AJ (2003) Morphometric analysis of brain structures for improved discrimination. MICCAI’03: Med Image Comput Comput Assist Interv, LNCS 2879:513-520, Springer, Heidelberg, 2003.

  157. Makedon FS, Tzika AA, Astrakas L, Pearlman JD, Wang Y, Steinberg T, Shen L, Chambers K, Ford JC (2003) Fusing information for tracking tumors. WCAO'03: 8th World Cong on Advances in Oncology and 6th Int Symp on Molecular Medicine, Hersonissos, Greece, Oct 16-18, 2003.

  158. Shen L, Ford JC, Makedon FS, Saykin AJ (2003) Hippocampal shape analysis: Surface-based representation and classification. SPIE-MI'03: SPIE Medical Imaging, 5032:253-264, San Diego, Feb 2003.

  159. Shen L, Ford JC, Makedon FS, Saykin AJ (2003) Effective classification of 3D closed surfaces: Application to modeling neuroanatomical structures. CVPRIP’03: Int Conf on Computer Vision, Pattern Recognition and Image Processing, pp 708-711, Cary, NC, September 26-30, 2003. 

  160. Steinberg T, Wang Y, Makedon FS, Shen L, Saykin AJ, Wishart HA (2003) A spatio-temporal multi-modal data management and analysis environment for tracking MS lesions. SSDBM'03: Int Conf on Scientific & Statistical Database Management, pp 245-246, Cambridge, MA, July 9-11, 2003.

  161. Ye S, Makedon FS, Steinberg T, Shen L, Wang Y, Zhao Y, Ford JC (2003) SCENS: a system for the mediated sharing of sensitive data. JCDL'03: The 3rd ACM/IEEE-CS Joint Conf on Digital Libraries, pp 263-265, Houston, TX, May 27-31, 2003.

  162. Makedon FS, Wang Y, Steinberg T, Wishart HA, Saykin AJ, Ford JC, Ye S, Shen L (2003) A system framework for the integration and analysis of multi-modal spatio-temporal data streams: A case study in MS lesion analysis. EMBS-NN'03: The 1st Int IEEE EMBS Neural Engineering Conf, pp 495-498, Capri Island, Italy, March 20-22, 2003.

  163. Makedon FS, Ford JC, Shen L, Steinberg T, Saykin AJ, Wishart HA, Kapidakis S (2002) MetaDL: A digital library of metadata for sensitive or complex research data. ECDL'02: European Conf on Digital Libraries, LNCS 2458, pp 374-389, Rome, Italy, September 16-18, 2002.

  164. Ford JC, Makedon FS, Shen L, Steinberg T, Saykin AJ, Wishart HA (2002) Evaluation metrics for user-centered ranking of content in metaDLs. EDL'02:The 4th DELOS Workshop on Evaluation of Digital Libraries: Testbeds, Measurements, Metrics, Budapest, Hungary, May 6-7, 2002.

  165. Ford JC, Shen L, Makedon FS, Flashman LA, Saykin AJ. (2002) A combined structural-functional classification of schizophrenia using hippocampal volume plus fMRI activation. EMBS-BMES'02: The 2nd Joint Meeting of the IEEE Engineering in Medicine and Biology Society and the Biomedical Engineering Society, 1:48-49, Houston, Texas, October 23-26, 2002.

  166. Shen L, Cheng L, Teng F, Makedon FS, Ford JC, Steinberg T, Saykin AJ (2001) A multimedia system for tracing and studying regions-of-interest in brain images. MTAC'01: IEEE Multimedia Technology and Application Conf, pp 238-245, Irvine, CA, November 7-9, 2001.

  167. Cheng L, Shen L, Makedon FS, Donnelly AM, Flashman LA, Saykin AJ (2001) On the development of a training and research system for tracing human brain structures. SCI'01: The 5th World Multi-Conf on Systemics, Cybernetics and Informatics, VIII:151-155, Orlando, FL, July 22-25, 2001.

  168. Shen L, Cheng L, Ford JC, Makedon FS, Megalooikonomou V, Steinberg T (2000) Mining the most interesting web access associations. WebNet'00: World Conf on the WWW and Internet, pp 489-494, San Antonio, TX, Oct. 30 - Nov. 4, 2000.

  169. Steinberg T, Shen L, Cheng L, Makedon FS. Tracking human expression actions in lectures (2000) ED-MEDIA'00: World Conf on Educational Multimedia, Hypermedia & Telecommunication, pp 1090-1095, Montreal, Quebec, Canada, June 26 - July 1, 2000.

  170. Steinberg T, Cheng L, Shen L, Makedon FS, Owen C, Ottmann T (1999) A model for authoring for retrieval (AFR): Retrieval on parallel data streams of recorded lecture information. SPIE-PE'99: Multimedia Storage and Archiving Systems IV, SPIE Photonics EAST, pp 423-430, Boston, MA, September 19-22, 1999.

  171. Shen L, Shen H, Cheng L. (1999) New algorithms for efficient mining of association rules. IEEE Frontiers'99: The 7th Symp on the Frontiers of Massively Parallel Computation, pp 234-241, Annapolis, Maryland, February 21-25, 1999.

  172. Shen L, Shen H, Pritchard P, Topor R (1998) Finding the N largest itemsets. ICDM'98: Int Conf on Data Mining, pp 211-222, Rio de Janeiro, RJ, Brazil, September 2-4, 1998.

  173. Shen L, Shen H (1998) Mining flexible multiple-level association rules in all concept hierarchies. DEXA'98: The 9th Int Conf on Database and Expert Systems Applications, Lecture Notes in Computer Science (LNCS) 1460:786-795, Vienna, Austria, August 24-28, 1998.