Conference papers (* indicates the corresponding author):

• Zhiguo Zhou, Rongfang Wang, Jing Yang, Jinkun Guo, “Multimodal weighted network for 3D brain tumor segmentation in MRI images”, SPIE Medical Imaging Conference, 2021

• D Feng, X Chen, Z Zhou*, H Liu, Y Wang, L Bai, S Zhang, X Mou^*, “A preliminary study of predicting effectiveness of anti-VEGF injection using OCT images based on deep learning”, 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020

• A Qasem, G Qin, J Wang, Z Zhou*, “Automatic tumor segmentation in digital breast tomosynthesis using U-net”, AAPM Annual Meeting, 2020 (Science Council Session - Data-Driven Automation and Decision Making)

• X Chen, M Zhou, K Wang, Z Wang, Z Zhou*, “Predicting Treatment Outcome After Immunotherapy Based On Delta-Radiomic Model in Metastatic Melanoma”, AAPM Annual Meeting, 2020 (Science Council Session - Data-Driven Automation and Decision Making)

• L Chen, M Dohopolski, Z Zhou, K Wang, R Wang, DJ Sher, J Wang, “Segmentation Guided Classification Scheme for Lymph Node Malignancy Prediction in Head and Neck Cancer”, ASTRO Annual Meeting, 2020

• L Chen, M Dohopolski, Z Zhou, K Wang, R Wang, D Sher, J Wang, “Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer”, AAPM Annual Meeting, 2020 (Oral presentation, Science Council Session - Data-Driven Automation and Decision Making)

• K Wang, Z Zhou, L Chen, R Wang, D Sher, J Wang, “Head Neck Cancer Locoregional Recurrence Prediction Using Delta-Radiomics Feature”, AAPM Annual Meeting, 2020

• J Yang, R Wang, Y Weng, L Chen, Z Zhou, “A Hierarchical 3D U-Net for Brain Tumor Substructure Segmentation”, AAPM Annual Meeting, 2020 (Science Council Session - Data-Driven Automation and Decision Making)

• Zhiguo Zhou, Michael Folkert, You Zhang, Steve Jiang, Jing Wang, “Journal Paper: Multi-objective based radiomics feature selection for lesion malignancy classification”, International Symposium on Biomedical Imaging (ISBI), 2020 (Video Presentation)

• Zhiguo Zhou, Xi Chen, Meijuan Zhou, “Predicting treatment outcome after immunotherapy through automated multi-objective delta-radiomics model in melanoma”, International Symposium on Biomedical Imaging (ISBI), 2020 (Video Presentation)

• Zhiguo Zhou, Michael Dohopolski, Liyuan Chen, Xi Chen, Steve Jiang, David Sher, Jing Wang, “Reliable lymph node metastasis prediction in head & neck cancer through automated multi-objective model”, IEEE International Conference on Biomedical and Health Informatics (BHI), 2019 (Oral presentation, top conference in Biomedical informatics, oral acceptance rate: 11%, overall acceptance rate: 31%)

• Z Zhou, M Dohopolski, L Chen, X Chen, S Jiang, D Sher, J Wang, “AutoMO: An Automated Multi-Objective Model for Reliably Predicting Lymph Node Metastasis in Head & Neck Cancer”, AAPM Annual Meeting, 2019 (Oral presentation, Science Council Session - Data Science in Medical Physics)

• Z Zhou, K Wang, H Liu, D Sher, J Wang, “Multifaceted Radiomics: Towards More Reliable Radiomics for Predicting Distant Metastasis in Head & Neck Cancer”, AAPM Annual Meeting, 2019 (Oral presentation, Science Council Session - Data Science in Medical Physics)

• Zhiguo Zhou, Genevieve Maquilan, Kimberly Thomas, Michael Folkert, Kevin Albuquerque, Jing Wang, “Predicting distant failure after radiotherapy in cervix cancer via automated multi-objective model”, International Conference on the Use of Computers in Radiation Therapy (ICCR), 2019 (Oral presentation)

• Zhiguo Zhou, Genggeng Qin, Pingkun Yan, Hongxia Hao, Steve Jiang, Jing Wang, “A shell and kernel descriptor based joint deep learning model for predicting breast lesion malignancy”, SPIE Medical Imaging Conference, 2019

• Xi Chen, Zhiguo Zhou (Co-first author), Kimberly Thomas, Michael Folkert, Nathan Kim, Asal Rahimi, Jing Wang, “A reliable multi-classifier multi-objective model for predicting recurrence in triple negative breast cancer”, 41th International Engineering in Medicine and Biology Conference (EMBC), 2019

• X Chen, Z Zhou, R Hannan, K Thomas, P Kapur, J Brugarolas, I Pedrosa, J Wang, “A Reliable Multi-Classifier Multi-Objective Model for Predicting Gene Mutation in Clear Cell Renal Cell Carcinoma”, AAPM Annual Meeting, 2019 (Oral presentation, Science Council Session - Data Science in Medical Physics)

• R Wang, Y Weng, Z Zhou, L Chen, J Wang, “Multi-Objective Ensemble Deep Learning for Predicting Outcome After Lung Cancer Radiotherapy Using Electronic Health Records”, AAPM Annual Meeting, 2019 (Oral presentation, Science Council Session - Data Science in Medical Physics)

• Zhiguo Zhou, Liyuan Chen, David Sher, Qiongwen Zhang, Jennifer Shah, Nhat-Long Pham, Steve Jiang, Jing Wang, “Predicting lymph node metastasis in head and neck cancer using many-objective radiomics”, ASTRO Annual Meeting, 2018 (Oral presentation, about 12% acceptance rate for oral in over 3000 submissions)

• Qiongwen Zhang, Zhiguo Zhou, Genggeng Qin, Ping Li, David Sher, Jing Wang, Steve Jiang, “Prediction of Local Persistence/Recurrence on PET/CT scans After Radiation Therapy Treatment of Head and Neck Cancer Using a Multi-objective Radiomics Model”, ASTRO Annual Meeting, 2018 (Oral presentation, about 12% acceptance rate for oral in over 3000 submissions)

• Zhiguo Zhou, Liyuan Chen, David Sher, Qiongwen Zhang, Jennifer Shah, Nhat-Long Pham, Steve Jiang, and Jing Wang, “Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensioal Convolutional Neural Network through Evidential Reasoning”, 40th International Engineering in Medicine and Biology Conference (EMBC), 2018, arXiv:1805.07021 (Oral presentation, 43% acceptance rate for oral)

• Z. Zhou, D. Sher, Q. Zhang, J. Shah, N. Pham, L. Chen, M. Folkert, S. Jiang, J. Wang, “Early Prediction of Locoregional Recurrence in Head & Neck Cancer After Radiation Therapy Through Multifaceted Radiomics”, AAPM Annual Meeting, 2018 (Oral presentation, featured as Science Highlights)

• Z. Zhou, S. Li, H. Hao, X. Chen, M. Folkert, S. Jiang, J. Wang, “A Multi-Objective Based Feature Selection Method for Lung Nodule Malignancy Classification”, AAPM Annual Meeting, 2018 (Oral presentation, featured as Science Highlights and Certificate Course)

• H. Hao, Z. Zhou, S. Li, M. Folkert, L. Yang, P. Iyengar, K. Westover, J. Wang, “Extended Shell Feature: Influence of Tumor Extension in Distant Metastasis Prediction for Non-Small Cell Lung Cancer”, AAPM Annual Meeting, 2018 (Oral presentation)

• L. Chen, Z. Zhou, D. Sher, Q. Zhang, J. Shah, N. Pham, S. Jiang, J. Wang, “Multi-Modality Convolutional Neural Network for Lymph Node Metastasis Prediction in Head and Neck Cancer”, AAPM Annual Meeting, 2018 (Oral presentation)

• Q. Zhang, Z. Zhou, G. Qin, P. Li, J. Shah, N. Pham, S. Gottumukkala, Z. Moore, D. Sher, J. Wang, S. Jiang, “Prediction of Local Persistence/Recurrence After Radiation Therapy Treatment of Head and Neck Cancer From PET/CT Using a Multi-Objective Radiomics Model”, AAPM Annual Meeting, 2018

• G. Qin, Z. Zhou, Y. Xu, J. Ma1, Q. Zhang, D. Nguyen, J. Wang, L. Zhou, W. Chen, S. Jiang, “Predicting Malignant Mass in Digital Breast Tomosynthesis Using a Multi-Objective Radiomics Model”, AAPM Annual Meeting, 2018

• S. Li, L. Chen, Z. Zhou, H. Hao, Y. Duan, B. Li, M. Folkert, S. Jiang, J. Wang, “Lung Nodule Malignancy Prediction by Combining Handcrafted Features and Deep Convolutional Neural Network”, AAPM Annual Meeting, 2018 (Oral presentation)

• G. Qin, H. Chen, H. Zeng, Y. Xu, Z. Zhou, Q. Zhang, D. Nguyen, W. Chen, L. Zhou, S. Jiang, “Mass Detection and Segmentation in Digital Breast Tomosynthesis Via Deep-Learning”, AAPM Annual Meeting, 2018

• Z. Zhou, M. Folkert, P. Iyengar, K. Westover, H. Choy, R. Timmerman, S. Jiang, J. Wang, “Multi-modality radiomics model for predicting distant failure in lung SBRT”, AAPM Annual Meeting, 2017

• Z. Zhou, G. Maquilan, K. Thomas, M. Folkert, K. Albuquerque, J. Wang, “Multi-classifier radiomics model for predicting distant failure in cervical cancer using PET image features”, AAPM Annual Meeting, 2017

• X. Chen, Z. Zhou, K. Thomas, M. Folkert, N. Kim, A. Rahimi, J. Wang, “Predicting recurrence in triple negative breast cancer patients from clinical parameters using different classifiers”, AAPM Annual Meeting, 2017

• H. Hao, Z. Zhou, S. Li, M. Folkert, K. Westover, P. Iyengar, L. Yang, J. Wang, “Shell feature: A new descriptor for predicting distant failure in lung SBRT”, AAPM Annual Meeting, 2017 (Oral presentation)

• S. Li, B. Li, Z. Zhou, N. Yang, H. Hao, M. Folkert, K. Westover, P. Iyengar, R. Timmerman, H. Choy, S. Jiang, J. Wang, “A support tensor machine based algorithm for distant failure prediction in lung SBRT”,  AAPM Annual Meeting, 2017 (Oral presentation)

• L. Chen, G. Maquilan, K. Thomas, C. Shen, Z. Zhou, M. Folkert, K. Albuquerque, J. Wang, “A semi-automatic algorithm for segmenting cervical tumors in 3D 18FDG PET”, AAPM Annual Meeting, 2017 (Oral presentation)

• Hongxia Hao, Zhiguo Zhou, Jing Wang, “Distant failure prediction for early stage NSCLC by analyzing PET with sparse representation”, SPIE Medical Imaging, Computer-Aided Diagnosis, 2017

• Zhiguo Zhou, Michael Folkert, Puneeth Iyengar, Faith Zhang, Kenneth Westover, Jing Wang, “Predicting distant failure in lung SBRT using multi-objective radiomics model”, ASTRO Annual Meeting, 2016

• Zhiguo Zhou, Michael Folkert, Puneeth Iyengar, Yuanyuan Zhang, Jing Wang, A multi-objective radiomics model for predicting distant failure in early stage NSCLC with SBRT, 18th International Conference on the use of Computers in Radiation Therapy (ICCR), 2016 (Oral presentation)

• Z. Zhou, M. Folkert, P. Iyengar, Y. Zhang, J. Wang, “Predicting distant failure in lung SBRT using multi-objective radiomics model”, AAPM Annual Meeting, 2016

• X. Chen, Z. Zhou, K. Thomas, J. Wang, “Predicting gene mutations in renal cell carcinoma based on imaging features: validation using TCGA-TCIA datasets”, AAPM Annual Meeting, 2016 (Oral presentation)

• L. Chen, Z. Zhou, J. Wang, “A geometrical constrained Chan-Vese based tumor segmentation scheme for PET”, AAPM Annual Meeting, 2016 (Oral presentation)

• Genevieve Maquilan, Kimberly Thomas, Zhiguo Zhou, Jing Wang, Michael R. Folkert, Kevin Albuquerque, “Clinical and PET Parameters as Prognostic Factors for Patients with Cervical Carcinoma: Clinical Implications of a Predictive Model Generated by a Support Vector Machine”, ASTRO Annual Meeting, 2016 (Oral presentation)

• Z. Zhou, N. Cannon, M. Folkert, P. Iyengar, H. Choy, R. Timmerman, S. Jiang, and J. Wang, “Predicting Distant Failure in Lung SBRT Using Clinical Parameters”, ASTRO Annual Meeting, 2015 (Oral presentation)

• Z. Zhou, N. Cannon, M. Folkert, P. Iyengar, H. Choy, R. Timmerman, S. Jiang, J. Wang, “Predicting Distant Failure in Lung SBRT Using Clinical Parameters”, AAPM Annual Meeting, vol. 42, pp. 3701 ,2015 (Oral presentation)

 

Peer-reviewed Journal Papers

(Google scholar:https://scholar.google.com/citations?user=bjClJewAAAAJ&hl=en)

• Zhiguo Zhou, Genevieve M. Maquilan, Kimberly Thomas, Jason Wachsmann, Jing Wang, Michael R. Folkert, Kevin Albuquerque, “Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients with Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning”, Technology in Cancer Research & Treatment, accepted, 2021 (IF: 2.074)

• Zhiguo Zhou, Kai Wang, Michael Folkert, Hui Liu, Steve Jiang, David Sher, Jing Wang, “Multifaceted radiomics for distant metastasis prediction in head & neck cancer”, Physics in Medicine and Biology, Vol. 65, 155009, 2020 (IF: 2.883) (Featured by Physics World Magazine and Interviewed by University of Central Missouri)

• Zhiguo Zhou, Shulong Li, Genggeng Qin, Michael Folkert, Steve Jiang, Jing Wang, “Multi-objective based radiomic feature selection for lesion malignancy classification”, IEEE Journal of Biomedical and Health Informatics, 24 (1), 194-204, 2020 (IF: 5.180) (Annual acceptance rate: 17.5% from 1446 submissions)

• Kai Wang, Zhiguo Zhou, Rongfang Wang, Liyuan Chen, Qiongwen Zhang, David Sher, Jing Wang, “A multi-objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancers”, Medical Physics, DOI: https://doi.org/10.1002/mp.14388, 2020 (IF: 3.317)

• Zhi-long Wang, Li-li Mao, Zhiguo Zhou, Lu Si, Hai-tao Zhu, Xi Chen, Mei-juan Zhou, Ying-shi Sun, Jun Guo, “Pilot study of CT-based radiomics model for early evaluation of response to immunotherapy in patients with metastatic melanoma”, Frontiers in Oncology, 10, 1524, 2020 (IF: 4.848)

• Chenyang Shen, Dan Nguyen, Zhiguo Zhou, Steve Jiang, Bin Dong, Xun Jia, “An introduction to deep learning in medical physics: advantages, potential, and challenges”, Physics in Medicine and Biology, 65, 5, 05TR01, 2020 (IF: 2.883)

• Guanyu Hu, Zhijie Zhou, Changhua Hu, Bangcheng Zhang, Zhiguo Zhou, Guozhu Wang, “Hidden behavior prediction of complex system based on time-delay belief rule base forecasting model”, Knowledge-Based Systems, Vol 203, 5, 160147, 2020. (IF: 5.921)

• Rongfang Wang, Yaochung Weng, Zhiguo Zhou, Liyuan Chen, Hongxia Hao, Jing Wang, “Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy”, Physics in Medicine and Biology, 64, 24, 245005, 2019 (IF: 2.883)

• Benjuang Yang, Yingjiang Wu, Zhiguo Zhou, Shulong Li, Genggeng Qin, Liyuan Chen, Jing Wang, “A collection input based support tensor machine for lesion malignancy classification in digital breast tomosynthesis”, Physics in Medicine and Biology, 64, 23, 235007, 2019 (IF: 2.883)

• Liyuan Chen, Zhiguo Zhou (Co-first author), David Sher, Qiongwen Zhang, Jennifer Shah, Nhat-Long Pham, Steve Jiang, Jing Wang, “Combining Many-objective Radiomics and 3-dimensional Convolutional Neural Network through Evidential Reasoning to Predict Lymph Node Metastasis in Head and Neck Cancer”, Physics in Medicine and Biology, 64 (7), 2019 (IF: 2.883) (Reported by Physics World Magazine)

• Liyuan Chen, Chenyang Shen, Zhiguo Zhou, Kevin Albuquerque, Michael Folkert, Jing Wang, “Automatic PET cervican tumor segmentation by combining deep learning and anatomic prior”, Physics in Medicine and Biology, 64, 8, 085019, 2019 (IF: 2.883)

• X. Liang, L. Chen, D. Nguyen, Z. Zhou, X. Gu, M. Yang, J. Wang, S. Jiang, “Generating Synthesized Computed Tomography (CT) from Cone-Beam Computed Tomography (CBCT) using CycleGAN for Adaptive Radiation Therapy”, Physics in Medicine and Biology, 64, 12, 125002, 2019 (IF: 2.883) (“Roberts Best Paper Prize”)

• Zhen Tian, Allen Yen, Zhiguo Zhou, Chenyang Shen, Kevin Albuquerque, Brain Hrycushko, “A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies”, Brachytherapy, 18, 4, 530-538, 2019 (IF: 1.853)

• Shulong Li, Panpan Xu, Bin Li, Liyuan Chen, Zhiguo Zhou, Hongxia Hao, Yingying Duan, Michael Folkert, Jianhua Ma, Shiying Huang, Steve Jiang, Jing Wang, “Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features”, Physics in Medicine and Biology, 64, 17, 175012, 2019 (IF: 2.883)

• Bin Qian, Qian-Qian Wang, Rong Hu, Zhi-Jie Zhou, Chuan-Qiang Yu, Zhiguo Zhou, “An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement”, Journal of Ambient Intelligence and Humanized Computing, 10, 3, 841-850, 2019 (IF: 4.594)

• Zhijie Zhou, Zhichao Feng, Changhua Hu, Xiaoxia Han, Zhiguo Zhou, Gailiang Li, “A hidden fault prediction model based on belief rule base with power set and considering attribute reliability”, Science China Information Science, Vol. 62, Issue 10, 202202, 2019 (IF: 3.304)

• Xi Chen, Zhiguo Zhou*, Raquibul Hannan, Kimberly Thomas, Ivan Pedrosa, Payal Kapur, James Brugarolas, Xuanqin Mou and Jing Wang, “Reliably Predicting Gene Mutation in Clear Cell Renal Cell Carcinoma through Multi-classifier Multi-objective Radiogenomics Model”, Physics in Medicine and Biology,  63 (21), 2018 (IF: 2.883) (Corresponding author)

• S. Li, B. Li, Z. Zhou, N. Yang, H. Hao, M. Folkert, P. Iyengar, K. Westover, H. Choy, R. Timmerman, S. Jiang, and J. Wang, “A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT”, Medical Image Analysis, 50, 106-116, 2018 (IF: 11.148)

• Liyuan Chen, Chengyang Shen, Zhiguo Zhou, Genevieve Maquilan, Kimberly Thomas, Michael R. Folkert, Kevin Albuquerque, Jing Wang, “Accurate segmenting cervical tumor in PET based on similarity between adjacent slices”, Computers in Biology and Medicine, 97 (6), 30-36, 2018 (IF: 3.434)

• Hongxia Hao, Zhiguo Zhou, Shulong Li, Genevieve Maquilan, Michael R. Folkert, Puneeth Iyengar, Kenneth D. Westover, Kevin Albuquerque, Fang Liu, Hak Choy, Robert Timmerman, Lin Yang, Jing Wang, “Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer”, Physics in Medicine and Biology, 63, 2018 (IF: 2.883)

• Hang Wei, Pei-Li Qiao, Guanyu Hu, Zhi-Jie Zhou, Zhiguo Zhou, Xiaojing Yin, “A New BRB Model for Cloud Security States Prediction based on the Large-scale Monitoring Data”, IEEE Access, 6 (12), 11907-11920, 2018 (IF: 3.745)

• Hang Wei, Guan-Yu Hu, Zhi-Jie Zhou, Pei-Li Qiao, Zhi-Guo Zhou, You-Min Zhang, “A new BRB model for security-state assessment of cloud computing based on the impact of external and internal environments”, Computers & Security, 73 (3), 207-218, 2018 (IF: 3.579)

• F.J. Zhao, Z.J. Zhou, C.H. Hu, L.L. Chang, Z.G. Zhou, G.L. Li, “A new evidential reasoning-based method for online safety assessment of complex systems”, IEEE Transactions on Systems, Man, and Cybernetics: System, 48(6), 954-966, 2018 (IF: 9.309)

• Zhi-Jie Zhou, Guan-Yu Hu, Bang-Cheng Zhang, Chang-Hua Hu, Zhi-Guo Zhou, Pei-Li Qiao, “A model for Hidden behavior prediction of complex systems based on belief rule base and power set”, IEEE Transactions on Systems, Man, and Cybernetics: System, 8, 1-7, 2017 (IF: 9.309)

• Zhiguo Zhou, Michael Folkert, Puneeth Iyengar, Kenneth Westover, Yuanyuan Zhang, Hak Choy, Robert Timmerman, Steve Jiang, Jing Wang, “Multi-objective radiomics model for predicting distant failure in lung SBRT”, Physics in Medicine and Biology, 62, 4460-4478, 2017 (IF: 2.883)

• Gai-Ling Li, Zhi-Jie Zhou, Chang-Hua Hu, Lei-lei Chang, Zhi-Guo Zhou, Fu-Jun Zhao, “A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base”, Safety Science, 93, 108-120, 2017 (IF: 4.105)

• Zhi-Long Wang, Zhi-Guo Zhou, Ying Chen, Xiao-Ting Li, Ying-Shi Sun, “Support vector machine model of computed tomography for assessing lymph node metastasis in esophageal cancer with neoadjuvant chemotherapy”, Journal of Computer Assisted Tomography, 41 (3), 455-460, 2017 (IF: 1.301)

• Z. Zhou, M. Folkert, N. Cannon, P. Iyengar, K. Westover, H. Choy, R. Timmerman, S. Jiang, and J. Wang, “Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters”, Radiotherapy & Oncology, 119 (3), 501-504, 2016 (IF: 4.856)

• Leilei Chang, Zhijie Zhou, Yuan You, Longhan Yang, Zhiguo Zhou, “Belief rule based expert system for classification problems with new rule activation and weight calculation procedures”, Information Science, 4 (336), 75-91, 2016 (IF: 5.910)

• Guan-Yu Hu, Zhi-Jie Zhou, Bang-Cheng Zhang, Xiao-Jing Yin, Zhi Cao, Zhi-Guo Zhou, “A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm”, Applied Soft Computing, 11 (48), 404-418, 2016 (IF: 5.472)

• Zhi-Jie Zhou, Lei-lei Chang, Chang-Hua Hu, Xiao-Xia Han, Zhi-Guo Zhou, “A new BRB-ER based model for assessing the lives of products using both failure data and expert knowledge”, IEEE Transactions on Systems, Man, and Cybernetics: System, 11 (46),1529-1543, 2016 (IF: 9.309)

• Zhi-Guo Zhou, Fang Liu, Ling-Ling Li, Li-Cheng Jiao, Zhi-Jie Zhou, Jian-Bo Yang, Zhi-Long Wang, “A cooperative belief rule based decision support system for lymph node metastasis diagnosis in gastric cancer”, Knowledge-based systems, 9 (85), 62-70, 2015 (IF: 5.921)

• Xiaozhuo Luo, Fang Liu, Shuyuan Yang, Xiaodong Wang, Zhiguo Zhou, “Joint Sparse regularization based Sparse Semi-Supervised Extreme Learning Machine (S3ELM) for classification”, Knowledge-based Systems, 73 (1), 149-160, 2015 (IF: 5.921)

• Zhi-Guo Zhou, Fang Liu, Li-Cheng Jiao, Zhi-Jie Zhou, Mao-Guo Gong, Xiao-Peng Zhang, “A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer”, Knowledge-based systems, 54, 128-136, 2013 (IF: 5.921)

• Zhi-Guo Zhou, Fang Liu, Li-Cheng Jiao, Xiao-Dong Wang, Shui-Ping Gou, Shuang Wang, “Object information based interactive segmentation for fatty tissue extraction”, Computers in Biology and Medicine, 43 (10), 1462-1470, 2013 (IF: 3.434)

• Zhi-Guo Zhou, Fang Liu, Li-Cheng Jiao, Zhi-Long Wang, Xiao-Peng Zhang, Xiao-Dong Wang, Xiao-Zhuo Luo, “An evidential reasoning based model for diagnosis of lymph node metastasis in gastric cancer”, BMC Medical Informatics and Decision Making, 13 (123), 2013 (IF: 2.067)

• Xiao-Dong Wang, Fang Liu, Li-Cheng Jiao, Zhi-Guo Zhou, Jing-Jing Yu, Bing Li, Jian-Rui Chen, Jiao Wu, Fan-Hua Shang, “An evidential reasoning based classification algorithm and its application for face recognition with class noise”, Pattern Recognition, 45 (12), 4117-4128, 2012 (IF: 7.196)

• Xiao-Zhuo Luo, Fang Liu, Xiao-Dong Wang, Zhi-Guo Zhou, “Clustering by local label approximation with extreme learning machine”, International Journal of Digital Content Technology & its Applications, 6 (18), 508-515, 2012s