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范明 教授

自动化学院（人工智能学院）

职务：

毕业院校： 中科院自动化所
邮件： mfan@hdu.edu.cn
办公地点：

10 访问

  • 个人资料
  • 社会职务
  • 研究领域
  • 教学与课程
  • 科研项目
  • 论文
  • 科研成果
  • 著作
  • 专利成果
  • 荣誉及奖励
  • 软件成果
个人简介

  范明，男，博士，教授，博士生导师。在西北工业大学自动化学院分别获得学士和硕士学位，在中国科学院自动化研究所模式识别国家重点实验室获得博士学位，在沙特阿卜杜拉国王科技大学应用数学与计算机学院开展博士后研究。获浙江省杰出青年科学基金项目资助、为“浙江省高校领军人才”培养对象，浙江省级人才计划、杭州电子科技大学星曜杭电工程之“科研之星”获得者、“心目中的好导师”团队负责人。

主要研究方向为医学影像分析、多组学大模型计算、人工智能、深度学习方法技术。主持国家自然科学基金3项、浙江省自然科学基金3项(其中重点项目1项)，参与国家重点基础研究发展计划（973）（2项）、国家自然科学基金重点项目（2项）等（均排名第2）。为国家自然科学基金、浙江省自然科学基金函评专家；SCI期刊Frontiers in Genetics客座编辑；中国生物医学工程学会医学图像与控制专委会理事、图形图像学会医学影像专委会委员、人工智能学会会员。近年来以第一或通信作者在包括Nature communications、Medical Image Analysis、Radiology、IEEE Journal of Biomedical and Health Informatics（4篇）、European Radiology、Breast Cancer Research、Briefings in Bioinformatics、Bioinformatics、npj systems biology and applications、Journal of Magnetic Resonance Imaging、NMR in Biomedicine等国际顶级或知名SCI学术期刊上发表论文数十篇。出版英文著作（章节）一部，申请国家专利12项，获软件著作权2项。担任Information Fusion、IEEE Journal of biomedical and Health informatics、Medical physics、IEEE Transactions on Bioinformatics and Computational Biology、European Journal of Radiology等在内的国际权威期刊审稿人。

培养的研究生获国家奖学金、优秀毕业生、优秀硕士论文，研究生人工智能竞赛一等奖、全国数模竞赛一等奖等。毕业生入职阿里巴巴、华为、中兴、海康威视等企业或研究所、高校等事业单位。



教育经历

Ÿ  中国科学院自动化研究所模式识别国家重点实验室，博士 

Ÿ   西北工业大学自动化学院控制与网络研究所，硕士 

Ÿ  西北工业大学自动化学院，生物医学工程，学士


工作经历

Ÿ  2020-01至现在，杭州电子科技大学，自动化学院(人工智能学院)，教授

Ÿ  2015-01至2019-12，杭州电子科技大学，生命信息与仪器工程学院，副教授

Ÿ  2013-08至2014-12，杭州电子科技大学，生命信息与仪器工程学院，讲师

Ÿ  2011-05至2013-04，阿卜杜拉国王科技大学，计算机、电子工程与应用数学学院, 博士后，合作导师：高欣教授


社会职务


 1. 生物医学工程学会医学图像信息与控制分会理事、图形图像学会医学影像专委会委员、Frontiers in Genetics客座编辑，中国人工智能学会会员。

 2. 担任IEEE Transactions on Medical Imaging、IEEE Journal of biomedical and Health informatics、Medical physics、IEEE Transactions on Bioinformatics and Computational Biology、European Journal of Radiology等在内的国际权威期刊审稿人。


研究领域

医学影像智能计算与人工智能技术、大模型分析等。通过利用医学图像等多组学数据，发展人工智能、图像识别、深度学习等方法技术，应用肿瘤智能诊疗中

教学与课程

  

     教改项目：

    杭州电子科技大学《应用统计学》研究生核心课程建设，2016年9月-2018年7月，已结题

Ø          杭州电子科技大学《生物医学信息学导论》国际化建设，2017年9月-2018年7月已结题

    课程：

     本科生：《数字图像处理》、《医学图像处理实践》

     研究生： 《应用统计学》、《生物信息学》


纵向科研

1. 浙江省自然科学基金杰出青年基金项目，肿瘤智能解析方法与精准诊疗研究，主持

2.国家自然科学基金面上项目，基于肿瘤微环境多尺度解析的乳腺癌智能诊疗研究，主持

3.国家自然科学基金面上项目，基于多模态影像组学的乳腺肿瘤多指标协同诊疗模型研究，主持

4.基于多粒度深度影像组学的乳腺癌基因表达特征预测模型研究, 浙江省基金重点项目，主持

5.面向乳腺癌诊疗的影像特征与基因表达特征的关联研究, 国家自然科学基金青年基金，主持

6.面向肿瘤精准诊疗的影像基因组学方法研究，国家自然科学基金重点基金项目，第一参与 


横向科研

    1、中国信息通信研究院横向课题，影像与基因组学智能诊断方法研究，主持

     2、中国信息通信研究院横向课题，智能病灶识别技术方法研究，主持


论文
[1]. Jian Guan, Ming Fan*，Lihua Li*, MVNMF: Multiview Nonnegative Matrix  
Factorization for Radio-multigenomic Analysis in Breast Cancer Prognosis，
Medical Image Analysis, In Press. (一区 Top，IF=10.6)
[2]. Wei Song, Xiang Pan, Ming Fan*, Lihua Li*, Clinical knowledge integrated  
multi-task learning network for breast tumor segmentation and pathological  
complete response prediction, Biomedical Signal Processing and Control,2025,  
106: 107772.
[3]. Xiaobao Ding, Lin Zhang, Ming Fan* & Lihua Li*, Network-based transfer of  
pan-cancer immunotherapy responses to guide breast cancer prognosis，npj  
systems biology and applications, 2025, 11:4.
[4]. Yang Liu; Yiqi Zhu; Zhehao Gu; Jinshan Pan; Juncheng Li; Ming Fan; Lihua  
Li; Tieyong Zeng; Enhanced dual contrast representation learning with cell  
separation and merging for breast cancer diagnosis, Computer Vision and Image  
Understanding, 2024, 247:104065
[5]. Haixia Long, Hao Wu, Chaoliang Sun, Xinli Xu, Xu-Hua Yang, Jie Xiao,  
Mingqi Lv, Qiuju Chen, Ming Fan*, Biological mechanism of sex differences  
in mental rotation: Evidence from multimodal MRI, transcriptomic and  
receptor/transporter data. NeuroImage, 2024, 304: 120955. (一区 Top)
[6]. Xiaobao Ding, Lin Zhang, Ming Fan*, Lihua Li,TME-NET: an interpretable  
deep neural network for predicting pan-cancer immune checkpoint inhibitor  
responses，Briefings in Bioinformatics, 2024, 25(5), bbae410.
[7]. Haixia Long, Zihao Chen, Xinli Xu, Qianwei Zhou, Zhaolin Fang, Mingqi Lv,  
Xu-Hua Yang, Jie Xiao, Hui Sun*, Ming Fan*. Elucidating genetic and  
molecular basis of altered higher-order brain structure-function coupling in  
major depressive disorder, NeuroImage, 2024, 297: 120722.
[8]. Ming Fan, Kailang Wang, Da Pan, Xuan Cao, Zhihao Li, Songlin He, Sangma  
Xie*, Chao You, Yajia Gu & Lihua Li*, Radiomic analysis reveals diverse  
prognostic and molecular insights into the response of breast cancer to  
neoadjuvant chemotherapy: a multicohort study, Journal of Translational  
Medicine, 2024, 22: 637.  
[9]. Ming Fan, Xuan Cao, Fuqing Lü, Sangma Xie, Zhou Yu, Yuanlin Chen, Zhong  
Lü, ∗ and Lihua Li*，Generative adversarial network-based synthesis of  
contrastenhanced MR images from precontrast images for predicting histological characteristics in breast cancer，Physics in Medicine and Biology,  
2024, 69: 095002.
[10]. Liangliang Zhang, Ming Fan*, Lihua Li*, Deconvolution-based  
pharmacokinetic analysis to improve the prediction of pathological information  
of breast cancer, Journal of Imaging Informatics in Medicine. 2024，37, 13–24,.
[11]. Liangliang Zhang, Ming Fan*, Lihua Li*, Efficient estimation of  
pharmacokinetic parameters from breast dynamic contrast-enhanced MRI based  
on a convolutional neural network for predicting molecular subtypes, Physics  
in Medicine and Biology, 2023，68，245001.
[12]. Jian Guan, Ming Fan,∗ Lihua Li*, A weakly supervised NMF method to  
decipher molecular subtype-related dynamic patterns in breast DCE-MR images,  
Physics in medicine and biology, 2023, 68, 215002.
[13]. Ming Fan, Guangyao Huang, Junhong Lou, Xin Gao, Tieyong Zeng, Lihua Li，
Cross-parametric generative adversarial network-based magnetic resonance  
image feature synthesis for breast lesion classification, IEEE Journal of  
Biomedical and Health Informatics, 2023,27 (11), 5495-5505.
[14]. Jian Guan, Ming Fan*, Tieyong Zeng and Lihua Li*, Learning Common and  
Task-specific Radiomic Features via Graph Regularized NMF for The Joint  
Prediction of Multiple Clinical Indicators in Breast Cancer. IEEE Journal of  
Biomedical and Health Informatics, 2023, 27, (10), 4792-4803.
[15]. Ming Fan, Xilin Wu, Jiadong Yu, Yueyue Liu, Kailang Wang, Tailong Xue,  
Tieyong Zeng, Shujun Chen* and Lihua Li*, Multiparametric MRI radiomics  
fusion for predicting the response and shrinkage pattern to neoadjuvant  
chemotherapy in breast cancer, Frontiers in Oncology, 2023, 13:1057841.
[16]. Ming Fan, Chengcheng Yuan, Guangyao Huang, Maosheng Xu, Shiwei Wang,  
Xin Gao, Lihua Li*, A framework for deep multitask learning with  
multiparametric magnetic resonance imaging for the joint prediction of  
histological characteristics in breast cancer, IEEE Journal of Biomedical and  
Health Informatics, 2022,26(8), 3884-3895 (IF=7.02)
[17]. Haixia long#, Ming Fan#, Qiaojun Li, Xuhua Yang, Yujiao Huang, Xinli Xu, Ji  
Ma, Jie Xiao, Tianzi Jiang, Structural and functional biomarkers of the insula  
subregions predict sex differences in aggression subscales, Human brain  
mapping, 2022, 1-13 (IF=5.038). 共同一作[18]. Liangliang Zhang, Ming Fan*, Shiwei Wang, Maosheng Xu, Lihua Li*,  
Radiomic analysis of pharmacokinetic heterogeneity within tumor based on the  
unsupervised decomposition of dynamic contrast enhanced MRI for predicting  
histological characteristics of breast cancer, Journal of Magnetic Resonance  
Imaging, 2022, 55(6), 1636-1647.
[19]. Ming Fan, Yajing Cui, You Chao, Li Liu, Yajia Gu, Weijun Peng, Qianming  
Bai, Xin Gao, Lihua Li, Radiogenomic signatures of Oncotype DX recurrence  
score predict survival in estrogen receptor-positive breast cancer: a multicohort  
study, Radiology, 2022, 302 (3), 516-524 (IF=29.1).  
[20]. Yane Li, Wei Yuan, Ming Fan, Bin Zheng, Lihua Li, Prediction of Short-Term  
Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four
View Mammograms, Journal of digital imaging, 2022, DOI10.1007/s10278-
019-00266-4 (IF=4.056).
[21]. Ming Fan, Wei Yuan, Weifen Liu, Xin Gao, Maosheng Xu, Shiwei Wang,  
Lihua Li, A deep matrix factorization framework for identifying underlying  
tissue-specific patterns of DCE-MRI: applications for molecular subtype  
classification in breast cancer. Physics in Medicine and Biology, 2021, 66(24):  
245013.
[22]. Ming Fan, You Zhang, Zhenyu Fu, Maosheng Xu, Shiwei Wang, Sangma Xie,  
Xin Gao, Yue Wang and Lihua Li*, A Deep Matrix Completion Method for  
Imputing Missing Histological Data in Breast Cancer by Integrating DCE-MRI  
Radiomics, Medical Physics, 2021, 48(12): 7685-7697.
[23]. Haixia Long#, Ming Fan#, Xuhua Yang, Qiu Guan, Yujiao Huang, Xinli Xu,  
Jie Xiao and Tianzi Jiang* Sex-related Difference in Mental Rotation  
Performance is Mediated by the special Functional Connectivity Between the  
Default Mode and Salience Networks, Neuroscience, 2021, 478: 65–74. (共同
第一)
[24]. Yu Li, Zeling Xu, Wenkai Han, Huiluo Cao, Ramzan Umarov, Aixin Yan, Ming  
Fan, Huan Chen, Carlos M. Duarte, Lihua Li, Pak-Leung Ho and Xin Gao*,  
HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic  
resistance genes, Microbiome, 2021, 9(1):40. (SCI 一区，Top 期刊，IF=11.6)
[25]. Ming Fan, Hang Chen, Chao You, Li Liu, Yajia Gu, Weijun Peng, Xin Gao and  
Lihua Li*. Longitudinal Dynamic Contrast Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast  
Cancer, Frontiers in Molecular Bioscience, 2021, 8:622219.（SCI三区，IF=5.2）
[26]. Lin Zhang, Ming Fan, Francesco Napolitano, Xin Gao, Ying Xu, and Lihua  
Li*，Transcriptomic analysis identifies organ-specific metastasis genes and  
pathways across different primary sites. Journal of Translational Medicine,  
2021, 19:31. （SCI 三区，IF=6.1）
[27]. Ming Fan, Pingping Xia，Robert Clarke, Yue Wang, Lihua Li*. Radiogenomic  
signatures reveal multiscale intratumor heterogeneity associated with biological  
functions and survival in breast cancer. Nature Communications, 2020, 11:4861.  
(SCI 一区，Top 期刊，IF=12.12)
[28]. Ming Fan, Wei Yuan, Wenrui Zhao, Maosheng Xu, Shiwei Wang, Xin Gao,  
and Lihua Li*. Joint Prediction of Breast Cancer Histological Grade and Ki-67  
Expression Level Based on DCE-MRI and DWI Radiomics. IEEE Journal of  
Biomedical and Health Informatics. 2020, 24(6), 1632-1642. (SCI 一区，TOP
期刊，IF=5.7)
[29]. Ming Fan*, Huizhong Zheng, Shuo Zheng, Chao You, Yajia Gu, Xin Gao,  
Weijun Peng, Lihua Li, Mass detection and segmentation in digital breast  
tomosynthesis using 3D-Mask region-based convolutional neural network: A  
comparative analysis. Frontiers in Molecular Biosciences, 2020, 7:599333.  
(SCI 三区)
[30]. Ming Fan, Zuhui Liu, Maosheng Xu, Shiwei Wang, Tieyong Zeng, Xin Gao,  
Lihua Li*, Generative adversarial networks-based super-resolution of diffusion
weighted imaging: application to tumour radiomics in breast cancer. NMR in  
Biomedicine, 2020, 33:e4345. (SCI 一区，Top 期刊)
[31]. Longxi Zhou, Zhongxiao Li, Juexiao Zhou, Haoyang Li, Yupeng Chen, Yuxin  
Huang, Dexuan Xie, Lintao Zhao, Ming Fan, Shahrukh Hashmi, Faisal  
AbdelKareem, Riham Eiada, Xigang Xiao, Lihua Li, Zhaowen Qiu, Xin Gao, A  
Rapid, Accurate and Machine-Agnostic Segmentation and Quantification  
Method for CT-Based COVID-19 Diagnosis. IEEE Transactions on Medical  
Imaging, 2020, 39 (8): 2638 - 2652. (SCI 一区，Top 期刊)
[32]. Ming Fan, Pingping Xia, Bing Liu, Lin Zhang, Yue Wang, Xin Gao, Lihua Li*:  
Tumour heterogeneity revealed by unsupervised decomposition of dynamic  
contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Breast  
Cancer Research, 2019, 21(1):112. (SCI 二区)
[33]. Ming Fan, Peng Zhang, Yue Wang, Weijun Peng, Shiwei Wang, Xin Gao,  
Maosheng Xu*, Lihua Li*. Radiomic analysis of imaging heterogeneity in  
tumours and the surrounding parenchyma based on unsupervised decomposition  
of DCE-MRI for predicting molecular subtypes of breast cancer. European  
Radiology. 2019, 29(8):4456-4467. (SCI 二区)
[34]. Ming Fan, Zuhui Liu, Sudan Xie, Maosheng Xu, Shiwei Wang, Xin Gao and  
Lihua Li*, Integration of dynamic contrast-enhanced magnetic resonance  
imaging and T2-weighted imaging radiomic features by a canonical correlation  
analysis-based feature fusion method to predict histological grade in ductal  
breast carcinoma, Physics in Medicine and Biology. 2019, 64(21), 215001. (SCI
三区)
[35]. Ming Fan, Yuanzhe Li, Shuo Zheng, Weijun Peng, Wei Tang, Lihua Li*,  
Computer-aided detection of mass in digital breast tomosynthesis using a faster  
region-based convolutional neural network. Methods. 2019, 166:103-111. (SCI
二区)
[36]. Yane Li, Ming Fan, Shichen Liu, Bin Zheng, Lihua Li, Prediction of Short
Term Breast Cancer Risk Based on Deep Convolutional Neural Networks in  
Mammography, Journal of Medical Imaging and Health Informatics 9: 1663–
1672, 2019. (SCI)
[37]. Yane Li, Ming Fan, Hu Cheng, Peng Zhang, Bin Zheng, Lihua Li, Assessment  
of global and local region-based bilateral mammographic feature asymmetry to  
predict short-term breast cancer risk. Physics in Medicine & Biology, 63,  
025004, 2018. (SCI 三区)
[38]. Ming Fan, Hu Cheng, Peng Zhang, Xin Gao, Juan Zhang, Guoliang Shao, and  
Lihua Li, DCE-MRI Texture Analysis with Tumor Subregion Partitioning for  
Predicting Ki-67 Status of Estrogen Receptor-Positive Breast Cancers. Journal  
of Magnetic Resonance Imaging, 2018;48(1):237-247 (SCI 二区)
[39]. Ming Fan, Ting He, Peng Zhang, Hu Cheng, Juan Zhang, Xin Gao, Lihua Li*,
Diffusion-Weighted Imaging Features of Breast Tumours and the Surrounding  
Stroma Reflect Intrinsic Heterogeneous Characteristics of Molecular Subtypes  
in Breast Cancer, NMR in Biomedicine, 2018, 31(2), e3869. (SCI 一区，TOP
期刊)

 


科研成果

1. 浙江省自然科学基金杰出青年基金项目，肿瘤智能解析方法与精准诊疗研究，主持

2.国家自然科学基金面上项目，基于肿瘤微环境多尺度解析的乳腺癌智能诊疗研究，主持

3.国家自然科学基金面上项目，基于多模态影像组学的乳腺肿瘤多指标协同诊疗模型研究，主持

4.基于多粒度深度影像组学的乳腺癌基因表达特征预测模型研究, 浙江省基金重点项目，主持

5.面向乳腺癌诊疗的影像特征与基因表达特征的关联研究, 国家自然科学基金青年基金，主持

6.面向肿瘤精准诊疗的影像基因组学方法研究，国家自然科学基金重点基金项目，第一参与 


著作

[1].      Ming Fan, Yitan Zhu, Lihua Li, Robert Clarke, Yue Wang (2019). Biomedical Information Technology (second edition), Chapter 18: Biomedical Image Characterization and Radiogenomics, Elsevier.


专利成果

1. 一种乳腺磁共振影像的病灶区域识别标注及疗效预测方法，202211054747.6，范明，宋伟，厉力华

2. 基于深度网络融合模型的乳腺癌病理图像分类方法，202211225012.5，范明;刘世博;厉力华。

3. 基于非负矩阵分解联合预测乳腺癌多个临床指标的方法, 202211015445.8, 范明，关健，厉力华，已公开

4. 一种基于乳腺DCE-MRI的药代动力学分析方法，202210996385.6，范明，张亮亮，厉力华，已公开

5. 基于影像动态增强模式的乳腺癌化疗疗效预测模型，202210988539.7，范明，苏天放，厉力华，已公开

6. 一种乳腺癌新辅助化疗疗效预测模型的构建方法，202210988553.7，范明，刘鑫，厉力华，已公开

7. 一种个体样本细胞亚群特异性表达的肿瘤预后分子标志物筛选方法，202210928358.5， 范明，张文锦，厉力华，已公开

8. 乳腺癌新辅助化疗后MRI影像生成及疗效预测方法，202210433788.6，范明，万超，厉力华，已公开

9. 一种T2加权影像特征生成动态增强影像特征的方法，202210174552.9，范明，楼俊鸿，厉力华，已公开

10.一种基于感知损失的乳腺MRI影像时间序列生成方法，202210058765.5，范明，吕福庆，厉力华，已公开

11.基于彩图分析的非接触毛细血管血气参数测定方法及其应用，202111556781.9，周康琪;范明;厉力华;李驰野;施钧辉，已公开

12. 数字乳腺断层影像病灶定位装置，202111434425.X，范明，简嘉豪，厉力华，郑慧中。已公开

13.  一种基于Hadoop的区域医学影像存储系统CN201510686905. 2016-01

14.  基于云计算的乳腺钼靶计算机辅助检测及远程诊断系统CN201510436930.6   2015-12


荣誉及奖励

1.浙江省级人才项目获得者、浙江省杰出青年基金获得者

2.获浙江省高校领军人才培养计划支持

3.杭州电子科技大学“西湖学者”

4.杭州电子科技大学“星曜杭电工程”之“科研之星”获得者

5.杭州电子科技大学“心目中的好导师”获得者


软件成果

1. 乳腺癌肿块影像检测软件       2019SR0759743 2019-05

2. DBT影像肿块智能诊断软件   软件著作权       2019SR0763615 2019-05