cai hongmin in scholat-k8凯发旗舰

hongmin cai ph.d, professor 

school of computer science & engineering 

south china university of technology (top university series (985,211)) 

email:hmcai@scut.edu.cn 

homepage: http://www2.scut.edu.cn/bioinformatics/

sep., 09 2003 – nov., 2007 university of hong kong, hong kong 

phd, applied mathematics 

sep., 09 2001 – sep., 2003 harbin institute of technology 

msc, applied mathematics 

sep., 09 1997 – nov., 2001 harbin institute of technology 

bsc, information and computational science

jun. 2019 -oct. 2019 institute of chemical research, kyoto university 

guest professor 

sep. 2016 –current school of computer science & engineering 

south china university of technology, china. 

professor 

jul. 2015 -sep. 2015 national laboratory of bioinformatics, tsinghua university 

visiting professor 

jun. 2013 -aug. 2013 institute of chemical research, kyoto university 

guest scholar 

apr. 2012 –sep. 2016 school of computer science & engineering 

south china university of technology, china. 

associate professor 

sep. 2008 -apr. 2012 school of information science and technology, the sun yat-sen university, china. 

assistant professor, tutor for master degree 

jun. 2006 -dec. 2006 section for biomedical image analysis 

university of pennsylvania, usa 

visiting scholar 

apr. 2005 -oct. 2005 center for bioinformatics, 

harvard university, usa 

visiting scholar

biomedical image segmentation and posterior analysis 

gene sequencing analysis 

bioinformatics data integration 

computational biology and bioinformatics 

machine learning and artificial intelligence

reviewers： ieee transactions on image processing, ieee transactions on medical image, medical image analysis, computerized medical imaging and graphics, pakdd, neurocomputing, bmc bioinformatics, ieee tkde, ieee tbe. brief in bioinformatics, nature communications, clinical cancer research 

conference pc for ： 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 

conference chair：danth 2014/2013，icdke 2012, cbc 2019 

guest editor: frontiers in genetics 

associate editor: interdisciplinary sciences - computational life sciences, current chinese biotechnology

[1]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. (if 5.288). 

[2]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. (if 1.78). 

[3]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. (if 3.505). 

[4]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.（if 0.682） 

[5]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.(if 3.64, jcr 3) 

[6]崔春艳, 李立, 蔡宏民, 田海英, 刘立志, 张敏,中国ct和mri杂志 9 (4) (2011) 35–38.( co-first author) 

[7]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.(if 3.73，jcr 3) 

[8]h. cai, z. yang, x. cao, w. xia, x. xu, a new iterative triclass thresholding technique in image segmentation, ieee transactions on image pro-cessing 23 (3) (2014) 1038–1046.(if 3.199，jcr 2) 

[9]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.(if 1.292，jcr 4) 

 
[10]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. (if 3.33，jcr 2) 

[11]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.(if 5.228, jcr 2，esi top cited) 

[12]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 

[13]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 

[14]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 

[15]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, 

[16]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 

[17]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 

[18]ou y, cai h, lee s k, et al. cascaded segmentation of brain tumors using multi-modality mr profiles[j]. international society for magnetic resonance in medicine (ismrm). 2007. 

[19]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 

 
[20]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 

 
[21]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. 

[22]田海英，蔡宏民*，赖剑煌，边缘检测新算子及其在去噪方面的应用，《第十五届全国图象图形学学术会议论文集》，2010，中国广州，2010.12.10-12.11 

[23]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 

[24]jiabing chen, et.al., hongmin cai *, “achieving accurate segmentation of nasopharyngeal carcinoma in mr images through recurrent attention”, miccai 2019, shenzhen, p.r. china 

[25]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). (if5.595, jcr 1) 

[26]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). (if2.473, jcr 2) 

[27]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). (if 1.545, jcr 4) 

[28]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. (if 3.37, jcr 3) 

[29]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. (if 1.545, jcr 4) 

[30]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. (if 2.635, jcr 3) 

[31]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). (if 1.512, jcr 3) 

[32]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). (if 1.770, jcr 3) 

[33]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. (if 3.962, jcr 1)

[34]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). (if 9.101, jcr1) 

[35]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). (if 9.101, jcr 1) 

[36]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). (if 3.577, jcr 2) 

[37]j. zeng, h. cai*, h. peng, h. wang, y. zhang, t. akutsu, causalcall: nanopore basecalling using a temporal convolutional network, frontiers in genetics (2019). (if 3.517, jcr 1) 

[38]a. 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. (if 4.151, jcr 2) 

[39]j. chen, h. peng, g. han, h. cai*, j. cai, hogmmc: a higher order graph matching with multiple network constraints model for gene–drug regulatory modules identification, bioinformatics 35 (4) (2018) 602–610. (if 5.481 jcr 1)

 
[40]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. (if 1.664，jcr 4) 

[41]x. yang, g. han, j. chen, h. cai*, finding correlated patterns via high order matching for multiple sourced biological data, ieee transactions on biomedical engineering 66 (4) (2018) 1017–1025. (if 3.577, jcr 2) 

[42]y. you, h. cai*, j. chen, low rank representation and its application in bioinformatics, current bioinformatics 13 (5) (2018) 508–517. (if 0.54, jcr 4) 

[43]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 (2018). (if 1.955, jcr 3) 

[44]z. wei, c. shu, c. zhang, j. huang, h. cai*, a short review of variants calling for single-cell-sequencing data with applications, the international journal of biochemistry & cell biology 92 (2017) 218–226. 

[45]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 (2017). (if 1.955, jcr 3) 

[46]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. (if 3.577, jcr 2) 

[47]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. (if 3.02, jcr 3) 

[48]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. (if 1.014, jcr 4) 

[49]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. (if 5.008, jcr 1)

 
[50]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. (if 3.02, jcr 3) 

[51]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. (if 3.02, jcr 3) 

 
[52]x.-b. wan, y. zhao, x.-j. fan, h.-m. cai#, y. zhang, m.-y. chen, j. xu, x.-y. wu, h.-b. li, y.-x. zeng, et al., molecular prognostic prediction for locally advanced nasopharyngeal carcinoma by support vector machine integrated approach, plos one 7 (3) (2012) e31989.（if 4.411, co-first author） 

[53]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. 

[54]chen p, huang w, shao w, hongmin cai*, “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 

[55]li tengpeng, zhang changsheng, bo xu, li fuqiang, and cai hongmin*，“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

 
[56]bo xu, zhang changsheng, xi yang, and cai hongmin*，“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]weihen huang, hongmin cai*, et. al., “mdagenera: an efficient and accurate simulator for multiple displacement amplification”, icic 2016, lanzhou, p.r. china, 2016.8.2-8.5 

[58]changsheng zhang, hongmin cai *, 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 

[59]jingying huang, yuwen zhou, hongmin cai*, 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 

[60]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. (if 17.73, jcr 1) 

[61]w. rong, e. zhuo, h. peng, j. chen, h. wang, h. cai*, learning a consensus affinity matrix for multi-view clustering via subspaces merging on grassmann manifold, information science (2020), accepted. (if 5.910, jcr 1) 

[62]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). (if 4.438, jcr 1) 

[63]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. (if 4.438, jcr 1) 

[64]z. li, z. zhang, j. qin, s. li, h. cai, low-rank analysis-synthesis dictionary learning with adaptively ordinal locality, neural networks (2019). (if 5.785, jcr 1) 

[65]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. (if 3.517, jcr 1) 

[66]w. wen, w. zhou, h. cai*, enhancing multi-view clustering through common subspace integration by considering both global similarities and local structures, neurocomputing (2019). (if 2.39, jcr 2) 

[67]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. (if 2.39，jcr 2) 

[68]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. (if 0.87, jcr 3) 

[69]cai h，“improvements over adaptive local hyperplane to achieve better classification”，icdm 2011, vancouver, canada, 2011.12.11-12.14 

 
[70]cai h，michale ng,“feature selection by relief through local hyperplane approximation”, pakdd 2012, , kuala lumpur, malaysia, 2012.5.29-6.1 

[71]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

cai hongmin, quality
enhancement and segmentation for biomedical images, lap lambert academic publishing gmbh & co. kg, 2011

2019.07.4 “integration of multiple sourced radiomics and omics data for cancer subtyping”，japan kyoto university institute for chemical research 

2019.05.29 “integration of multiple sourced radiomics and omics data for cancer subtyping”，department of mathematics, hong kong baptist university 

 
2019.04.28 “enhancing multi-view clustering through common subspace integration by considering both global similarities and local structures”，seminar on mathematics and machine learning theory and methods in image processing, wuhan, china 

2019.04.20 “integration of omics and imaging data for subtyping and drug analysis” , the 4th symposium on biomedical engineering of young pi, chendu, china 

2019.03.24 “cancer subtyping by omics data integration”, the 6th national conference in bioinformatics and computational biology, chendu, china 

2019.03.21 “survival patterns revealed from multi-modalities mri for npc patients”,  university of electronic science and technology of china, , chendu, china 

2019.01.12 “the recent advances in medical image analysis”, harbin institute of technology, harbin 

2018.06.02 “hopes: an omics data integration method based on high order path elucidated similarity for cancer classification”, guangdong biological information community, guangzhou 

2018.04.13 “hogmmnc: a higher order graph matching with multiple network constraints model for gene-drug regulatory modules identification”, institute of mathematical sciences, renmin university of china 

2017.12.28 “wavedec: an image incited approach to identify both shared and individual patterns of copy-number variations”, university of macau 

2017.10.13-10.15 “identifying many-to-many relationships between gene-expression data and drug-response data via sparse binary matching”, the second ccf bioinformatics conference (cbc 2017), changsha 

2017.05.20-05.21 “analysis of macroscopic medical image phenotype to microcosmic genotype multi-source heterogeneous data”, the fifth interdisciplinary study of mathematics, computer and life science young scholar forum，beijing 

2017.04.08-04.09 “multi - source data association module search based on tensor matching ”, china bioengineering society 2nd youth technology forum，guangzhou 

 
2016.12.15-12.18 “multi-norm constrained optimization methods for calling copy number variants in single cell sequencing data”, ieee international conference on bioinformatics and biomedicine (bibm 2016), shenzhen 

2016.08.02-08.05 “copy number variants calling by multi-constrained optimization”，the twelfth international conference on intelligent computing (icic 2016), lanzhou 

2016.07.01-07.03 “jnco: a jointly norm constrained optimization to identify both recurrent and individual copy number variations from multisample acgh”, academic conference on bioinformatics and intelligent information processing, changchun 

2016.03.31-04.03 “macro-to-micro omics data integration: relating genotype to phonotype”， the ninth international conference on the frontiers of information technology, application and tools (fitat 2016), keynote speaker, zhuhai 

2013.08.21 “feature weighting via local hyperplane approximation”, department of computer science, hong kong university of science and technology 

2012.08.19 “feature weighting for gene sequencing data”, school of physics and chemistry, kyoto university, japan 

2011.08.29 “feature weighting via local hyperplane approximation”, department of mathematics, hong kong baptist university