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蔡宏民, 華南理工大學計算機學科學與技術學院教授、博士生導師，京都大學客座教授，2016科技部重點領域創新團隊“機器智能創新團隊”成員，廣東省重點實驗室成員，2014廣東省優秀青年教師，京都大學客座教授。全國系統生物學專業委員會委員，生物信息學與人工生命專業委員會委員、常委委員，CCF生物信息學專委會委員、常委委員。1997年09-2003年07月在哈爾濱工業大學獲得本科、碩士學位。2007年11月香港大學數學系取得博士學位。2012年9月至今在華南理工大學任教，2016年9月破格晉升博士生導師，同年破格晉升教授。哈佛大學、賓夕法尼亞大學訪問學者，京都大學、香港浸會大學和清華大學生物信息國家重點實驗室高級訪問學者。ISB 2019/2018/2017/2016/2015/2014，ISBRA 2018/2017/2016, ICIC 2019/2018/2017/2016, BESC 2018, CBC 2018/2017/2016, CIBB2015, GIW 2017/2018/2019國際會議程序委員。DANTH 2014/2013，ICDKE 2012的國際會議共同主席，CCF-CBC 2019會議組織主席。應邀在國內外做~50次會議報告。在國際頂級雜志及一流會議上發表論文60多篇，包括Bioinformatics、Neuroimage、IEEE Trans Image Processing、IEEE Trans. Biomedical Engineering、European Radiology，Neural Networks。主持或完成國家級、省部級項目十多項。 研究興趣包括醫學圖像分析與理解、多源生物數據信息分析和人工智能理論及醫學大數據應用。

2003 年 09 月 – 2007  年 09 月                    香港大學, 香港，理學博士 應用數學       導師: Prof. S.P. Yung

2001 年 09 月 – 2003年 07 月                       哈爾濱工業大學，理學碩士 應用數學       導師: 吳從炘教授

1997 年 09 月 – 2001年 07 月                        哈爾濱工業大學，理學學士 信息與計算科學

2019 年 06月－2019年10月，                             京都大學，客座教授

2016 年 09月－至今                                              華南理工大學，教授（破格），博士生導師（破格）

2012 年 03月－2016年09月                               華南理工大學， 副教授，先上崗教授

2013 年 06 月－2013年09月                                Institute of Chemical Research，京都大學，日本，訪問教授

2008 年 09 月－2012年03月                              中山大學 信息科學與技術學院，講師、碩士導師

2006 年 06 月－2006 年 12 月                           Section for Biomedical Image，Analysis, 賓州大學(UPenn),  美國，訪問學者

2005 年 04 月- 2005 年 10月                             Centerfor Bioinformatics, 哈佛大學， 美國 ，研究員

?  生物醫學圖像信息挖掘

?  生物醫學表型和多組學整合分析

?  腫瘤組學大數據分析

?  人工智能與模式識別

ISBRA
2018/2017/2016/2015/2014, BESC 2018, CBC 2018/2017/2016, CIBB2015,GIW2019， BIBM 2016/2017/2018/2019mso-hansi-font-family:'Georgia', Georgia, 'Times New Roman', Times, 'Microsoft YaHei', SimSun, SimHei, serif;"Times New Roman"">等國際會議程序委員。DANTH
2014/2013"Times New Roman"">，ICDKE 2012的國際會議共同主席；CCF-CBC2019會議組織主席

"Times New Roman"">全國生物信息學與人工生命專業委員會委員、常任委員；全國CCF生物信息學專委會委員、常任委員；全國系統生物學專業委員會委員；全國自動化協會智能健康與生物信息學專委會委員；廣東省轉化醫學眼科分會副主委；廣東省精準醫學應用學會-"Times New Roman"">數字智能化分會副主任委員；CIPSmso-hansi-font-family:'Georgia', Georgia, 'Times New Roman', Times, 'Microsoft YaHei', SimSun, SimHei, serif;"Times New Roman"">醫療健康與生物信息專委；廣東省計算機協會大數據專業委員會委員

Interdisciplinary Sciences: Computational Life
Sciences mso-hansi-font-family:'Georgia', Georgia, 'Times New Roman', Times, 'Microsoft YaHei', SimSun, SimHei, serif;"Times New Roman";color:#4472C4;mso-bidi-font-weight:
bold">副主編；Current Chinese Science mso-bidi-font-weight:bold">編委；Frontiers in Genetics mso-hansi-font-family:'Georgia', Georgia, 'Times New Roman', Times, 'Microsoft YaHei', SimSun, SimHei, serif;"Times New Roman";color:#4472C4">客座編委

期刊論文： 

 
[1] Bin Zhang, Hongmin Cai*, Jiazhou Chen, Yu Hu, Jie Huang, Wentao Rong, Wanlin Weng,Qinjian Huang, Haiyan Wang, Hong Peng, Fast and Accurate Clustering of Multiple Modality Data via Feature Matching. IEEE Transactions on Cybernetics, 2020, accepted.（中科院和JCR分區分別為1區和1區，影響因子11.079) 

[2] H. Peng, J. Chen, Y. Hu, H. Cai*, Integrating Tensor Similarity to Enhance Clustering Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, accepted. （中科院和JCR分區分別為1區和1區，影響因子17.73) 

[3] W. Rong, E. Zhuo, H. Peng, J. Chen, H. Wang, H. Cai*, Learning a consensus a?nity matrix for multi-view clustering via subspaces merging on Grassmann manifold, Information Science, 2020, accepted. (中科院和JCR分區分別為1區和1區，影響因子 5.910) 

[4] H. Wang, G. Han, B. Zhang, G. Tao, H. Cai*, Exsavi: Excavating Both Sample-wise and View-wise Relationships to Boost Multi-view Subspace Clustering. Neurocomputing, 2020, accepted. （中科院和JCR分區分別為2區和1區，影響因子4.438) 

[5] H. Wang, G. Han, H. Li, G. Tao, E. Zhuo, L. Liu, H. Cai*, Y. Ou, Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multi-modalities MR Sequences. Computational and Mathematical Methods in Medicine, 2020, accepted.（中科院和JCR分區分別為4區和3區，影響因子1.770)

 
[6] J. Yang, X. Dong, Y. Hu, H. Cai* et al, Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks. Interdisciplinary Sciences: Computational Life Sciences, 2020, accepted. （中科院和JCR分區分別為3區和3區，影響因子1.512) 

[7] Q. Huang, Y. Zhang, H. Peng, T. Dan, H. Cai*, Deep Subspace Clustering to Achieve Jointly Latent Feature Extraction and Discriminative Learning, Neurocomputing, 404 (3) (2020) 340-350. （中科院和JCR分區分別為2區和1區，影響因子4.438) 

[8] X. Chen, M. He, T. Dan, N. Wang, M. Lin, L. Zhang, J. Xian, H. Cai* and H. Xie, Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images using Deep Learning, Frontiers in Neurology (2020)1664-2295. （中科院和JCR分區分別為3區和3區，影響因子2.635） 

[9] Z, Wei, Y. Zhang, W. Weng, J. Chen, H. Cai*, Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets, Briefings in Bioinformatics, bbaa102， 2020. （中科院和JCR分區分別為2區和1區，影響因子9.101） 

[10] J. Huang, J. Z. Chen, B. Zhang, L. Zhu, H. Cai*, Evaluation of gene-drug common module identification methods using pharmacogenomics data, Briefings in Bioinformatics. 2020.（中科院和JCR分區分別為2區和1區，影響因子9.101） 

[11] B. Xie, T. Lei, N. Wang, H Cai, et al. Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery, 15, 1303–1312 (2020). （中科院和JCR分區分別為4區和2區，影響因子2.473） 

[12] H. Xie, N. Wang, M. He, L. Zhang, H. Cai, J. Xian, M. Lin, J. Zheng and Y. Yang, Using deep learning algorithms to classify fetal brain ultrasound images as normal or abnormal. Ultrasound in Obstetrics Gynecology. 2020（中科院和JCR分區分別是1區和1區，影響因子5.595） 

[13] J. Zeng, H. Cai*, H. Peng, H. Wang, Y. Zhang, T. Akutsu, Causalcall: Nanopore Basecalling using A Temporal Convolutional Network, Frontiers in Genetics, 2019. （中科院和JCR分區分別是3區和1區，影響因子3.517） 

[14] W. Weng, W. Zhou, Z. Chen, H. Peng, H. Cai*, Enhancing multi-view clustering through common subspace integration by considering both global similarities and local structures, Neurocomputing, 378 (2019) 375-386. （中科院和JCR分區分別為2區和1區，影響因子4.438） 

[15] Z. Li, Z. Zhang, J. Qin, S. Li, H. Cai*, Low-rank analysis-synthesis dictionary learning with adaptively ordinal locality, Neural Networks. 2019.（中科院和JCR分區分別為2區和1區，影響因子5.785） 

[16] J. Chen, G. Han, A. Xu, H. Cai*, Identification of multidimensional regulatory modules through multi-graph matching with network constraints, IEEE Transactions on Biomedical Engineering. 2019.（中科院和JCR分區分別為2區和1區，影響因子4.491） 

[17] H. Cai, X. Pang, D. Dong, Y. Ma, Y. Huang, X. Fan, P. Wu, H. Chen, F. He, Y. Cheng, et al., Molecular decision tree algorithms predict individual recurrence pattern for locally advanced nasopharyngeal carcinoma, Journal of Cancer 10 (15) (2019) 3323. （中科院和JCR分區分別為3區和2區，影響因子3.182） 

[18] Xu, J. Chen, H. Peng, G. Han, H. Cai*, Simultaneous interrogation of cancer omics to identify subtypes with significant clinical differences, Frontiers in genetics 10 (2019) 236. （中科院和JCR分區分別為3區和1區，影響因子3.517）

 
[19] H. Cai, Q. Huang, W. Rong, Y. Song, J. Li, J. Wang, J. Chen, L. Li, Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms, Computational and mathematical methods in medicine. 2019. （中科院和JCR分區分別為4區和2區，影響因子1.563） 

[20] E. Zhuo, W. Zhang, H. Li, G. Zhang, B. Jing, J. Zhou, C. Cui, M Chen, Y. Sun, L. Liu, H. Cai*, Radiomics on multi-modalities mr sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (npc) into distinct survival subgroups, European radiology (2019) 1–10. （中科院和JCR分區分別為2區和1區，影響因子3.962） 

[21] Y. You, H. Cai*, J. Chen, Low rank representation and its application in bioinformatics, Current Bioinformatics 13 (5) (2018) 508–517. （中科院和JCR分區分別為4區和3區，影響因子1.189） 

[22] X. Yang, G. Han, J. Chen, H. Cai*, Finding correlated patterns via highorder matching for multiple sourced biological data, IEEE Transactions on Biomedical Engineering 66 (4) (2018) 1017–1025. （中科院和JCR分區分別為2區和1區，影響因子4.491） 

[23] X. Jiang, F. Xie, L. Liu, Y. Peng, H. Cai, L. Li, Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast-enhanced and diffusion-weighted mri, Oncology letters 16 (2) (2018) 1521–1528. （中科院和JCR分區分別為4區和3區，影響因子1.871） 

[24] J. Chen, H. Peng, G. Han, H. Cai*, J. Cai, Hogmmnc: a higher order graph matching with multiple network constraints model for gene–drug regulatory modules identification, Bioinformatics 35 (4) (2018) 602–610. （中科院和JCR分區分別為3 區和1區，影響因子4.531） 

[25] J. Li, Y. Song, S. Xu, J. Wang, H. Huang, W. Ma, X. Jiang, Y. Wu, H. Cai, L. Li, Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches, International Journal of Computer Assisted Radiology and Surgery 14 (4) (2019) 709–721. （中科院和JCR分區分別為3 區和1區，影響因子2.155） 

[26] H. Cai, P. Chen, J. Chen, J. Cai, Y. Song, G. Han, Wavedec: a wavelet approach to identify both shared and individual patterns of copy-number variations, IEEE Transactions on Biomedical Engineering 65 (2) (2017) 353–364. 5. （中科院和JCR分區分別為2區和1區，影響因子4.491） 

[27] J. Cai, H. Cai*, J. Chen, X. Yang, Identifying many-to-many relationships between gene-Expression data and drug-response data via sparse binary matching, IEEE-ACM transactions on computational biology and bioinformatics.2018. （中科院和JCR分區分別為3區和1區，影響因子2.896） 

[28] Z. Wei, C. Shu, C. Zhang, J. Huang, H. Cai*, A short review of variants calling for single-cell-sequencing data with applications, International journal of biochemistry & cell biology 92 (2017) 218–226. （中科院和JCR分區分別為3區和2區，影響因子3.144） 

[29] X. Yang, G. Han, H. Cai*, Y. Song, Recovering hidden diagonal structures via non-negative matrix factorization with multiple constraints, IEEE-ACM transactions on computational biology and bioinformatics.2017. （中科院和JCR分區分別為3區和1區，影響因子2.896） 

[30] B. Xu, H. Cai*, C. Zhang, X. Yang, G. Han, Copy number variants calling for single cell sequencing data by multi-constrained optimization, Computational biology and chemistry 63 (2016) 15–20.100. （中科院和JCR分區分別為4區和2區，影響因子1.581） 

[31] C. Zhang, H. Cai*, J. Huang, Y. Song, nbcnv: a multi-constrained optimization model for discovering copy number variants in single-cell sequencing data, BMC bioinformatics 17 (1) (2016) 384. （中科院和JCR分區分別為4區和1區，影響因子2.511） 

[32] J. Wang, X. Yang, H. Cai￡, W. Tan, C. Jin, L. Li, Discrimination of breast cancer with microcalcifications on mammography by deep learning, Scientific reports 6 (2016) 27327. （中科院和JCR分區分別為3區和1區，影響因子4.011, ESI 高引用論文） 

[33] R. Jiang, R. You, X.-Q. Pei, X. Zou, M.-X. Zhang, T.-M. Wang, R. Sun, D.-H. Luo, P.-Y. Huang, Q.-Y. Chen, H.-M. Cai￡, Development of a ten-signature classifier using a support vector machine integrated approach to subdivide the m1 stage into m1a and m1b stages of nasopharyngeal carcinoma with synchronous metastases to better predict patients’ survival, Oncotarget 90 7 (3) (2016) 3645. （中科院和JCR分區分別是1區和1區，影響因子5.168） 

[34] X. Cheng, H. Cai*, Y. Zhang, B. Xu, W. Su, Optimal combination of feature selection and classification via local hyperplane based learning strategy, BMC bioinformatics 16 (1) (2015) 219. （中科院和JCR分區分別為4區和1區，影響因子2.511） 

[35] H. Tian, H. Cai, J. Lai, A novel diffusion system for impulse noise removal based on a robust diffusion tensor, Neurocomputing 133 (2014) 222–230. （中科院和JCR分區分別為2區和1區，影響因子4.072） 

[36] H. Cai, L. Liu, Y. Peng, Y. Wu, L. Li, Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols, BMC cancer 14 (1) (2014) 366. （中科院和JCR分區分別為3區和2區，影響因子2.933） 

[37] X. Cheng, H. Cai*, P. He, Y. Zhang, R. Tian, Combination of effective machine learning techniques and chemometric analysis for evaluation of bupleuri radix through high-performance thin-layer chromatography, Analytical Methods 5 (22) (2013) 6325–6330. （中科院和JCR分區分別為3區和1區，影響因子2.378） 

 
[38] H. Cai, P. Ruan, M. Ng, T. Akutsu, Feature weight estimation for gene selection: a local hyperlinear learning approach, BMC bioinformatics 15 (1) (2014) 70. （中科院和JCR分區分別為4區和1區，影響因子2.511） 

[39] H. Cai, Z. Yang, X. Cao, W. Xia, X. Xu, A new iterative triclass thresholding technique in image segmentation, IEEE transactions on image processing 23 (3) (2014) 1038–1046. （中科院和JCR分區分別為1區和1區，影響因子6.790，下載前25%的明星論文） 

[40] H. Cai, Y. Peng, C. Ou, M. Chen, L. Li, Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted mr: a machine learning approach, PloS one 9 (1) (2014) e87387. （中科院和JCR分區分別為3區和1區，影響因子2.776）

 
[41] W. Su, H. Wu, Y. Li, J. Zhao, F. H. Lochovsky, H. Cai, T. Huang, Under-standing query interfaces by statistical parsing, ACM Transactions on the Web (TWEB) 7 (2) (2013) 8. （中科院和JCR分區分別為3區和2區，影響因子1.580）
[42] 崔春艷, 李立, 蔡宏民, 田海英, 劉立志, 張敏, 中國CT和MRI雜志 9 (4) (2011) 35–38.(并列第一) 

[43] C. Cui, H. Cai*, L. Liu, L. Li, H. Tian, L. Li, Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging, European radiology 21 (11) (2011) 2318–2325. （中科院和JCR分區分別為2區和1區，影響因子3.962） 

[44] X. Wan, Y. Zhao, X. Fan, H. Cai#, Y. Zhang, M. Chen, J. Xu, X. Wu, H. Li, Y. Zeng, et al., Molecular prognostic prediction for locally advanced nasopharyngeal carcinoma by support vector machine integrated approach, PloS one 7 (3) (2012) e31989. （中科院和JCR分區分別為3區和1區，影響因子2.776） 

[45] H. Cai, C. Cui, H. Tian, M. Zhang, L. Li, A novel approach to segment and classify regional lymph nodes on computed tomography images, Computational and mathematical methods in medicine 2012. （中科院和JCR分區分別為4區和2區，影響因子1.563） 

[46] H. Cai, X. Xu, J. Lu, J. Lichtman, S. Yung, S. T. Wong, Using nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3d optical microscopy images, Medical Image Analysis 12 (6) (2008) 666–675. （中科院和JCR分區分別為2區和1區，影響因子8.880） 

[47] R. Verma, E. I. Zacharaki, Y. Ou, H. Cai, S. Chawla, S.-K. Lee, E. R. Melhem, R. Wolf, C. Davatzikos, Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of mr images, Academic radiology 15 (8) (2008) 966–977. （中科院和JCR分區分別為3區和2區，影響因子2.267） 

[48] H. Cai, X. Xu, J. Lu, J. W. Lichtman, S. Yung, S. T. Wong, Repulsive force based snake model to segment and track neuronal axons in 3d microscopy image stacks, NeuroImage 32 (4) (2006) 1608–1620. （中科院和JCR分區分別為1區和1區，影響因子5.812） 

會議論文： 

[49] Chen J, Han G, Cai H *, Ma J, Kim M, Laurienti, P, Wu G. Estimating Common Harmonic Waves of Brain Networks on Stiefel Manifold, MICCAI 2020. 

 
[50] Fan Z, Dan T, Yu H, Liu B, Cai H *. Single Fundus Image Super-Resolution Via Cascaded Channel-Wise Attention Network, IEEE EMBC 2020 

[51] Zeng J, Cai H *， Akutsu T，Breast Cancer Subtype by Imbalanced Omics Data through A Deep Learning Fusion Model, 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics, 2020. Japan 

[52] Huang J, et.al., Cai H *, “Achieving Accurate Segmentation of Nasopharyngeal Carcinoma in MR Images through Recurrent Attention”, MICCAI 2019, Shenzhen, P.R. China 

[53] Huang J, Zhou Y, Cai H*, et. al, “A copy-number variation detection pipeline for single cell sequencing data on BGI online”, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)，Kansas City, MO, USA，2017.11.13-2017.11.16 

[54] Zhang C, Cai H *, et. Al., “Multi-norm Constrained Optimization Methods for Calling Copy Number Variants Calling in Single Cell Sequencing Data”, BIBM 2016, Shenzhen, P.R. China, 2016.12.15-12.18 

[55] Huang W, Cai H*, et. Al., “MDAGenera: An Efficient and Accurate Simulator for Multiple Displacement Amplification”, ICIC 2016, Lanzhou, P.R. China, 2016.8.2-8.5 

[56] Bo X, Zhang C, Xi Y, and Cai H*，“Copy Number Variants Calling for Single Cell Sequencing Data by Multi-constrained Optimization”, APBC 2016, San Francisco, the United States (U.S.), 2016.1.11-1.13 

[57] Li T, Zhang C, Bo X, Li F, and Cai H*，“MALBACsim: a Multiple Annealing and Looping Based Amplification Cycles Simulator”, BIBM 2015 Washington D.C., the United States (U.S.), 2015.11.9-11.12 

[58] Chen P, Huang W, Shao W, Cai H*, “Discrimination of recurrent CNVs from individual ones from multisample aCGH by jointly constrained minimization”, ACM BCB 2015, Atlanta, the United States (U.S.), 2015.9.9-9.12 

[59] Xu B, Li T, Luo Y, Xu R, Cai H. An Empirical Algorithm for Bias Correction Based on GC Estimation for Single Cell Sequencing[C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, 2014: 15-21. 

[60] Cai H，Michale Ng,“Optimal combination of feature weight learning and classification based on local approximation”, ICDKE 2012, Wuyishan, P.R. China, 2012.11.21-11.23 

[61] Cai H，Michale Ng,“Feature selection by RELIEF through local hyperplane approximation”, PAKDD 2012, , Kuala Lumpur, Malaysia, 2012.5.29-6.1 

[62] Cai H，“Improvements over adaptive local hyperplane to achieve better classification”，ICDM 2011, Vancouver, Canada, 2011.12.11-12.14（口頭報告） 

[63] H.Y Tian, Cai H*, Lai J, X.Y Xu, “Image noise removal based on a new edge indicator”, ICIP 2011, Brussels, Belgium, 2011.9.11-9.14 

[64] 田海英，蔡宏民*，賴劍煌，邊緣檢測新算子及其在去噪方面的應用，《第十五屆全國圖象圖形學學術會議論文集》，2010，中國廣州，2010.12.10-12.11 

[65] Tian H, Cai H, Lai J H, et al. Effective image noise removal based on difference eigenvalue[C]//2011 18th IEEE International Conference on Image Processing. IEEE, 2011: 3357-3360. 

[66] H. Y Tian, Cai H*, Lai J, “Improved Partial Differential Equation-based Method to Remove Noise in Image enhancement”, WIAMIS 2011: 12th International Workshop on Image Analysis for Multimedia Interactive Services, Delft, The Netherlands, 2011.4.13-4.15 

 
[67] H. Y Tian, Cai H*, Cui C, Lai J, Li L “Quality enhancement with adaptive edge preservation for lymph nodal images”, AIP Conference Proceedings, 2011 International Symposium on Computational Models for Life Sciences, Vol.1371(1), pp. 341-342, Toyama, Japan, 2011.10.11-10.13 

 
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[69] Zhang Y, Xu X, Cai H, Yung SP, and Wong STC, "New Nonlinear Diffusion Method to Improve Image Quality", IEEE International Conference on Image Processing, ICIP 2007, San Antonio, Texas, USA, 2007.9.16-9.19 

[70] Cai H, Xu X, Lu J, Lichtman J, Yung SP, and Wong STC, "Shape-constrained repulsive snake method to segment and track neurons in 3D microscopy images", Proc International Symposium of Biomedical Imaging ISBI 2006, pp. 538-541, Arlington, VA, USA, 2006.4.6-4.9 

[71] Chen J, Xu X, Cai H, Miller L, and Wong STC, "A new snake algorithm to track neuronal structure in microscopy image", Proceedings of the 2005 International Symposium on Intelligent Signal Processing and Communication Systems, pp. 537-541, Hong Kong, P.R. China, 

[72] Cai H, Verma R, Ou Y, Lee S, Melhem E.R, and Davatzikos C, "Probabilistic Segmentation of Brain Tumor on Multi-modaility MRI", Proc International Symposium of Biomedical Imaging ISBI 2007, pp:600 – 603, Washington D.C., USA, 2007.4.12-4.16 

[73] Cai H, Xu X, Lu J, Lichtman J, Yung SP, and Wong STC, "Use mean shift to track neuronal axons in 3D", Life Science Systems and Applications Workshop, IEEE/NLM, pp. 1-2, Bethesda, MD, USA, 2006.7.13-7.14 

[74] Cai H, Xu X, Lu J, Lichtman J, Yung SP, and Wong STC, "Segment and Track Neurons in 3D by Repulsive Snake Method", Proceedings of the 2005 International Symposium on Intelligent Signal Processing and Communication Systems, pp. 529-531, Hong Kong, P.R. China, 2005.12.13-12.16

專著                                                                                                                               

Cai Hongmin, Quality
enhancement and segmentation for biomedical images, LAP Lambert Academic Publishing GmbH & Co. KG, 2011