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Reliable Intelligence & Medical Innovation Lab

Publications

2026
  1. Kazi Md Farhad Mahmud, Rachel Yoder, Joshua Staley, Allison Aripoli, Shane Stecklein, Priyanka Sharma, Ahmad Qasem, Zhiguo Zhou*, “ER2Net: An evidential reasoning rule enabled neural network for reliable triple negative breast cancer tumor segmentation in Magnetic resonance imaging”, Journal of Medical Imaging, Vol 13, Issue 1, 014005, 2026.
2025
  1. Zhiguo Zhou, Hui Liu, “Evidential reasoning rule learning”, IEEE Transactions in Artificial Intelligence, Issue 5, page 1-10, 2025
  2. Xi Chen; Jiahuan Lv; Zeyu Wang; Genggeng Qin, “Adaptive-AutoMO: A domain adaptative automated multiobjective neural network for reliable lesion malignancy prediction via digital breast tomosynthesis”, Journal of Biomedical Informatics, accepted, 2025
  3. Hui Liu, Zhiguo Zhou*, “Deep evidential reasoning rule learning”, Signal Processing, Vol 233, 109984, 2025
  4. Ahmad Qasem, Zhiguo Zhou*, “Automated tumor localization and segmentation through hybrid neural network in head & neck cancer”, Medical Dosimetry, Vol 50, Issue 1, 2025
  5.  Wang, JS Chen, X Zhang, H Liu, Z Zhou, C Jiao, J Wang, “Language-guided multimodal domain generalization for outcome prediction of head and neck cancer”, Computers in Biology and Medicine, 197, 110992, 2025

  6. M Wang, Z Leng, P Hu, J Huang, B Wang, K Zhou, G Hu, B Yan, L Wu, Y Xu, Q Wan, Z Zhou, “FRCAE: Feature regularization meta-learning with channel-wise attention expansion”, Pattern Recognition, 112334, 2025

  7. Jie Wu, Xiao-ran Wang, Jun-cheng Zhou, Rong-rong Li, Yu-wang Chen, and Zhi-guo Zhou, An analysis on parameter optimization for a belief rules-based classification system, Journal of Control and Decision, 2025.

2024
  1. Weiyong Liu, Dongyue Wang, Le Liu, Zhiguo Zhou*, “Assessing the influence of B-US, CDFI, SE and patient age on predicting molecular subtypes in breast lesions using deep learning algorithms”, Journal of Ultrasound in Medicine, accepted, 2024
  2. Ahmad Qasem, Genggeng Qin, Zhiguo Zhou*, “AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis vis U-Net”, Journal of Medical Imaging, Vol 11 (2), 2024.
  3. Ahmad Qasem, Zhiguo Zhou*, “Automated tumor localization and segmentation through hybrid neural network in head & neck cancer”, Medical Dosimetry, accepted, 2024
  4. Xi Chen, Jiahuan Lv, Zeyu Wang, Genggeng Qin, Zhiguo Zhou*, Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis, Computers in Biology and Medicine, 183, 109299, 2024
  5. Rongfang Wang, Zhaoshan Mu, Jing Wang, Kai Wang, Hui Liu, Zhiguo Zhou, Licheng Jiao, “ASF-LKUNet: Adjacent-scale fusion U-Net with large kernel for multi-organ segmentation”, Computers in Biology and Medicine, Issue 181, 109050, 2024
2023
  1. Z. Wang, Q. Wang, J. Wu, M. Ma, Z. Pei, Y. Sun, Z. Zhou*, “An Ensemble Belief Rule Base Model for Pathologic Complete Response Prediction in Gastric Cancer”, Expert Systems With Applications, Vol 233, Issue 15, 2023
  2. X. Chen, X. Wang, J. Lv, G. Qin and Z. Zhou*, “An integrated network based on 2D/3D feature correlations for benign-malignant tumor classification and uncertainty estimation in digital breast tomosynthesis”, Physics in Medicine and Biology, 68, 175046, 2023

  3. Zhiguo Zhou, Liyuan Chen, Michael Dohopolski, David Sher and Jing Wang, “ARMO:  automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer”, Physics in Medicine and Biology, 68, 095012, 2023

  4. Dehua Feng, Xi Chen, Xiaoyu Wang, Xuanqin Mou, Ling Bai, Shu Zhang, Zhiguo Zhou, “Predicting effectiveness of anti-VEGF injection through self-supervised learning in OCT images”. Mathematical Biosciences and Engineering, 20 (2), 2439-2458, 2023

  5. Chen T., Yang L., Chen H., Zhou Z., Wu Z., Luo H., Li Q., Zhu Y., “A pairwise radiomics algorithm – lesion pair relation estimation (PRE) model for distinguishing multiple primary lung cancer (MPLC) from intrapulmonary metastasis (IPM)”, Precision Clinical Medicine, Vol 6 (4), 2023

2022
  1. Qiongwen Zhang, Kai Wang, Zhiguo Zhou, Genggeng Qin, Lei Wang, Ping Li, David Sher, Steve Jiang, Jing Wang, “Predicting local persistence/recurrence after radiation therapy for head and neck cancer from PET/CT using a multi-objective, multi-classifier radiomics model”, Frontiers in oncology, 12: 955712, 2022
  2. Jie Wu, Qianwen Wang, Zhilong Wang, Zhiguo Zhou*, “AutoBRB: An automated belief rule base model for pathologic complete response prediction in gastric cancer”, Computers in Biology and Medicine, 140, 105104, 2022
  3. Rongfang Wang, Jinkun Guo, Zhiguo Zhou, Kai Wang, Shuiping Gou, Rongbin Xu, David Sher, Jing Wang, “Locoregional recurrence prediction in head and neck cancer based on multi-modality and multi-view feature expansion”, Physics in Medicine & Biology, Vol 67, Issue 12, 2022
  4. Xi Chen, Jiahuan Lv, Dehua Feng, Xuanqin Mou, Ling Bai, Shu Zhang, Zhiguo Zhou*, “AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine”, International Workshop on Machine Learning in Medical Imaging Conference, 2022 (MLMI 2022)
  5. Zhiguo Zhou, Meijuan Zhou, Zhilong Wang, Xi Chen, “Predicting treatment outcome in metastatic melanoma through automated multi-objective model with hyperparameter optimization”, SPIE Medical Imaging Conference, 2022 (Oral Presentation)
  6. Ahmad Qasem, Hui Liu, and Zhiguo Zhou*, “Automated tumor localization and segmentation through hybrid neural network”, SPIE Medical Imaging Conference, 2022
  7. Dehua Feng, Xi Chen, Xiaoyu Wang, Jiahuan Lv, Ling Bai, Shu Zhang and Zhiguo Zhou, “Penalized Entropy: a novel loss function for uncertainty estimation and optimization in medical image classification”, IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), 2022 (Oral presentation)
2021
  1. Xi Chen, Meijuan Zhou, Zhilong Wang, Si Lu, Shaojie Chang, Zhiguo Zhou*, “Immunotherapy treatment outcome prediction in metastatic melanoma through automated multi-objective delta-radiomics model”, Computers in Biology and Medicine, 138, 104916, 2021
  2. Zhiguo Zhou, Rongfang Wang, Jing Yang, Jinkun Guo, “Multimodal weighted network for 3D brain tumor segmentation in MRI images”, SPIE Medical Imaging Conference, 2021
  3. Jinkun Guo, Rongfang Wang, Zhiguo Zhou, Kai Wang, Rongbin Xu, Jing Wang, “Multi-modality and Multi-view 2D CNN to Predict Locoregional Recurrence in Head & Neck Cancer”, International Joint Conference on Neural Network (IJCNN), 2021
2020
  1. 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, Vol 19: 1-9, 2020
  2. 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 (Featured by Physics World Magazine)
  3. 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
  4. 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, Vol 47, Issue 10, 5392-5400, 2020
  5. 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
  6. 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
  7. 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
2019
  1. 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
  2. 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
  3. Liyuan Chen, Zhiguo Zhou, 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 (Reported by Physics World Magazine)
  4. 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
  5. 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 (“Roberts Best Paper Prize”)
  6. 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
  7. 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
  8. 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)
  9. 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)
  10. 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
  11. Xi Chen#, Zhiguo Zhou#, 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
2018
  1. 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
  2. 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
  3. 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
  4. 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
  5. 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 (Oral presentation)
2017 and before
  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
KU School of Medicine

University of Kansas Medical Center
Department of Biostatistics and Data Science
3901 Rainbow Boulevard
Mailstop 1026
Kansas City, KS 66160