方强
基本信息:
方强，讲师
单位院系：浙江海洋大学海洋工程装备学院
电子邮件-Email：20240072@zjou.edu.cn
办公地点：浙江省舟山市定海区临城街道海大南路1号，文理楼312
个人教育及工作经历：
2017.09-2021.10：加拿大拉瓦尔大学 电子工程博士
2021.11-2023.10：加拿大卡尔加里大学 机器学习博士后研究员
2023.10-2024.09: 加拿大多伦多大学 人工智能研究科学家
2024.09-至今： 浙江海洋大学 讲师
学科方向：计算机科学与技术， 大数据与软件工程， 机械电子，机械工程，农业机械化，电气工程，自动化，控制工程
研究方向：大数据与人工智能、计算机视觉与图像处理，大语言模型与多模态算法，自然语言处理，人工智能与工程应用
主持科研项目：
1. 加拿大安大略省癌症研究基金, 乳腺癌免疫疗法反应预测项目, 70万元, 2023.10至2024.09, 主持, 已结题。
2. 加拿大国家卫生研究院 (CIHR), MRI图像分析与深度学习临床预测研究, 125万元, 2021.11至2023.08, 参与, 已结题。
3. AICE (加拿大阿尔伯塔创新组织), 深度学习个性化治疗决策优化研究, 60万元, 2021.11至2022.10, 主持, 已结题。
4. 加拿大自然科学与工程研究委员会 (NSERC), 先进热成像技术应用研究, 60万元, 2017.09至2019.10, 主持, 已结题。
5. Pratt & Whitney, AI 无损探测研究基金, 60万元, 2018至2020, 主持, 已结题。
6. 加拿大自然科学与工程研究委员会 (NSERC), 先进太赫兹成像技术与人工智能的应用研究, 72.5万元, 2019.01至2021.05, 参与, 已结题。
7. 加拿大自然科学与工程研究委员会 (NSERC), 高端红外热像仪研究, 50.5万元, 2018.01至2020.05, 参与, 已结题。
8. 浙江省舟山市，青年英才创新大赛，30万，主持
代表性论文及专著：
[1] Qiang Fang, Ryan J Choo, Yuping Duan, Yuxia Duan, Hongming Chen, Yun Gao, Zhiqun Mao, Ze Yu, Yunyan Zhang. Weakly-Supervised Natural Language Processing with BERT-Clinical for Automated Lesion Information Extraction from Free-Text MRI Reports in Multiple Sclerosis Patients [Molecular & Cellular Biomechanics, 2025]. (通讯及一作)
[2] Li, S., Omer, Qiang Fang (通讯作者), Duan, Y., et al. Deep-Optimal Leucorrhea Detection Through Fluorescent Benchmark Data Analysis [J Digit Imaging, Inform. med., 2025].
[3] Qiang Fang (通讯及一作), Ibarra-Castanedo C, Maldague X. Defect Enhancement and Image Noise Reduction Analysis Using Partial Least Square-Generative Adversarial Networks (PLS-GANs) in Thermographic Nondestructive Evaluation [Journal of Nondestructive Evaluation, 40.4, 2021]. (Impact score 2.7, cite score 4.5)
[4] Qiang Fang (通讯及一作), Xavier Maldague. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning [Appl. Sci., 2020, 10, 6819]. (Impact score 2.7, cite score 4.5)
[5] Qiang Fang (通讯及一作), Ibarra-Castanedo C, Maldague X. Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data [Big Data and Cognitive Computing, 2021, 5(1): 9]. (Impact score 3.7, cite score 4.9)
[6] Qiang Fang (通讯及一作), Ibarra-Castanedo C, Garrido I, et al. Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data [Sensors, 2023, 23(9): 4444]. (Impact score 3.9, cite score 6.8)
[7] Qiang Fang (通讯及一作), Maldague X. Images Noise Reduction and Defect Enhancement Using Convolutional Auto-Encoders in Pulsed Thermography Data [to be submitted for IEEE Transactions]. (Impact factor 8.3, cite score 13.6)
[8] Qiang Fang, Luanne Metz, Yunyan Zhang. Extraction of Lesion Activity Information in Multiple Sclerosis from Unformatted MRI Reports Using Advanced Natural Language Processing Techniques [Radiology: Artificial Intelligence, 2025, submitted]. (Impact score 7.41, cite score 4.5)
[9] Qiang Fang, Luanne Metz, Ryan Justin Choo, Yunyan Zhang. Predicting Treatment Response for Patients with Relapsing Remitting Multiple Sclerosis Using Advanced Deep Learning Algorithms [Radiology: Artificial Intelligence, 2025, accepted]. (Impact score 7.41, cite score 4.5)
[10] Qiang Fang, Luanne Metz, Yunyan Zhang. A Novel Approach for Lesion Activity Information Extraction in Multiple Sclerosis from Unformatted MRI Reports Using Transformer-Based Attention Mechanisms with BERT [Radiology: Artificial Intelligence, 2025, submitted]. (Impact score 7.41, cite score 4.5)
[11] Qiang Fang, Mao Zhi Qun, Yuxia Duan, Ze Yu. Multimodelling with Transformer for Disease Prediction [Macao Scientific Publishers, 2025]. (学术专著)
[12] Siyu Xiang, Fang Q, Yuxia Duan, et al. Automated Defect Classification in Infrared Thermography Based on a Neural Network [NDT & E International, 2019]. (Cite score 8.5, Impact score 4.2)
[13] Garrido I, Lagüela S, Fang Q, et al. Combination of Thermal Fundamentals and Deep Learning for the Automatic Thermographic Inspection of Thermal Bridges and Water-Related Problems in Infrastructures [Quantitative InfraRed Thermography Journal, 2022]. (Cite score 3.8, Impact score 2.5)
[14] Jingwei C, Fang Q, Yuxia Duan, et al. Automatic Crack Segmentation in Mural Using Optical Pulsed Thermography [Journal of Cultural Heritage, 2023]. (Cite score 6.6, Impact score 3.1)
[15] Siyu Xiang, Fang Q, Yuxia Duan, et al. A Reliability Study on Automated Defect Assessment in Optical Pulsed Thermography [Infrared Physics & Technology, 2023]. (Cite score 5.6, Impact score 3.3)
[16] Cui J, Tao N, Omer A M, Fang Q, et al. Attention-Enhanced U-Net for Automatic Crack Detection in Ancient Murals Using Optical Pulsed Thermography [Journal of Cultural Heritage, 2024, 70: 111-119]
[17] Zheng W, Zhang S, Omer A M, Fang Q, et al. A Novel Approach for One-Step Defect Detection and Depth Estimation Using Sequenced Thermal Signal Encoding [Nondestructive Testing and Evaluation, 2024, 39(8): 2606-2621]
[18] Tao Y, Hu C, Fang Q, Zhang H, et al. Automated Defect Detection in Non-Planar Objects Using Deep Learning Algorithms [Journal of Nondestructive Evaluation, 2022, 41(1): 14]
[19] Qiang Fang, Maldague X. Defect Visibility Enhancement of Step-Wage CFRP by Generative Adversarial Network with Dynamic Lin Scan Thermography: Experimental and Synthetic Data [NDT & E International, 2025]. (Cite score 8.5, Impact score 4.2)
[20] Qiang Fang, Maldague X. Defect Depth Estimation in Infrared Thermography by Deep Learning Algorithm with Experimental Pulsed Thermography Data [Infrared Physics & Technology, 2025]. (Cite score 5.6, Impact score 3.3)
[21] Qiang Fang, Ba Diep Nguyen, Clemente Ibarra Castanedo, Yuxia Duan, Xavier Maldague. Automatic Defect Detection in Infrared Thermography by Deep Learning Algorithm [SPIE Defense Commercial Sensing, 2020].
[22] Qiang Fang, Farima Abdolahi Mamoudan, Celment Ibarra Castanedo, Xavier Maldague. Defect Depth Estimation in Infrared Thermography by Deep Learning Algorithm [2020 Structural Health Monitoring & Nondestructive Testing Conference, 22nd to 23rd of November 2020, Quebec City, QC]
[23] Qiang Fang, Xavier Maldague, Iván Garrido, Jorge Erazo Aux, Clemente Ibarra-Castanedo. Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data [15th Quantitative InfraRed Thermography Conference, 21st to 30th of September 2020, University of Porto, Portugal]
[24] Qiang Fang, Clemente Ibarra Castanedo, Xavier Maldague. Automatic Segmentation and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data [20th World Conference on Non-Destructive Testing, Songdo Convensia, Incheon, South Korea]
[25] Qiang Fang, Clemente Ibarra Castanedo, Xavier Maldague. Université Laval Infrared Thermography Databases for Deep Learning Automatic Defect Detection Research and Analysis [16th International Workshop on Advanced Infrared Technology & Applications, 2021]
[26] Qiang Fang, Luanne Metz, Yunyan Zhang. Extraction of Lesion Activity Information in Multiple Sclerosis from Unformatted MRI Reports Using Advanced Natural Language Processing Techniques [Poster format, 38th Congress of the European Committee for Treatment and Research in Multiple Sclerosis, 2022, Amsterdam, Netherlands]
[27] Garrido González I, Lagüela López S, Fang Q. Combination of Thermal Fundamentals and Deep Learning for Infrastructure Inspections from Thermographic Images: Preliminary Results [15th Quantitative InfraRed Thermography Conference, 2020, University of Porto, Portugal]
[28] Garrido González I, Lagüela López S, Fang Q. Generation of Synthetic Thermographic Images Using Generative Adversarial Networks to Improve Automatic Thermographic Inspection of Infrastructures [Defense + Commercial Sensing 2022 On Demand, May 2-8, 2022]
授权发明专利：
[1] Qiang Fang, 一种基于Grabcut图像算法的智能称重系统, 2016, 中国发明专利, CN106525212.
学术专著:
[1] Qiang Fang, Zhi Qun Mao, Yuxia Duan, Ze Yu. Multimodelling with Transformer for Disease Prediction [Macao Scientific Publishers, 2025].
国际学术会议重要报告
[1] 阿尔伯塔MS网络论坛邀请演讲（2022年，班夫，加拿大阿尔伯塔省）
[2] 演讲主题：基于先进自然语言处理技术的多发性硬化症MRI报告中病变活动信息提取
[3] 第27届RIMS年度会议邀请演讲（2022年，阿姆斯特丹，荷兰）
[4] 多伦多大学机器学习分部邀请演讲（2021年，加拿大安大略省多伦多）
[5] OICR转化研究会议邀请演讲（2024年，乳腺癌亚型中癌症与免疫谱系细胞的空间模式识别，基于图形机器学习方法）
[6] 世界无损检测大会邀请演讲（2024年，韩国仁川）
主讲课程：
研究生课程：人工智能及其应用
获奖情况：
（1）国际光电工程学会最佳论文奖（SPIE 2021）
（2）第14届先进红外技术与应用国际研讨会最佳论文奖（AITA 2021）
（3）国际光电工程学会会议旅游基金（SPIE 2020）
（4）定量红外热成像国际会议最佳论文奖（QIRT 2020）
（5）定量红外热成像旅游基金（QIRT 2021）
（5）第27届RIMS年度会议差旅资助（2022年）
（6）阿尔伯塔创新博士后基金（Alberta Innovates Postdoctoral Recruitment Fellowships 2021）
学术兼职：
目前担任《Journal of Computational and Cognitive Engineering》和《Quantitative Infrared Thermography》等会议的评审委员，以及《Sensors》（红外技术专题）、《Robotics & Automation Engineering》和《Thermal Science and Engineering》等多个国际期刊的审稿人。
其他：
本人于2021 年获得加拿大拉瓦尔大学电子工程博士学位，师从加拿大工程院院士Xavier Maldague 教授，在 2021 至 2023 年间，作为机器学习博士后研究员，任职于加拿大卡尔加里大学的Hotchkiss 研究所，开发了多项影像数据与非结构文本数据多模态数据融合疾病预测算法。2023年-2024年任职于加拿大多伦多大学，担任人工智能研究科学家，主持多项融合人工智能技术与医学实际需求的跨学科研究项目现任浙江海洋大学海洋工程与装备学院教授。
致力于计算机视觉、人工智能、数据科学与机器学习在工程领域的深入应用。主要研究方向包括人工智能算法开发、图像与数据融合、深度学习、影像分析等，主持多项国家级、省级科研项目，涉及医疗、航空、海洋等多个行业应用。
目前，在《Journal of Nondestructive Evaluation》、《Journal of Imaging Informatics in Medicine》等国际顶级期刊上发表了30 余篇学术论文，拥有国家发明专利及AI 大模型预测算法的学术专著2 项。
参与并主导多项跨学科科研合作:多发性硬化症与乳腺癌的智能诊断系统、美国普惠公司-
人工智能驱动的自动化检测技术。
每年招收硕士研究生，欢迎对人工智能、机器学习、图像处理和大数据等领域有兴趣的同学加入课题组，携手开展前沿科技研究。
