Haigen Hu

Haigen Hu received the Ph.D. degree in Control theory and Control Engineering from Tongji University, Shanghai, China in 2013. He has come out from the post-doctoral station of LITIS Laboratory, University of Rouen Normandy, France in 2019. He is currently an associate professor in the College of Computer Science and Technology at Zhejiang University of Technology, China. His current research interests include deep learning, computer vision and medical image processing.

As a PI, he has been responsible for the general project of the NSFC, the sub-projects of the 863 project, the Natural Science Foundation of Zhejiang Province and many enterprise engineering projects. 

• 得同济大学控制理论与控制工程博士学位，2019年6月从法国鲁昂大学Laboratoire LITIS，Equipe Quantif博士后出站，现为浙江工业大学计算机科学与技术学院副教授，硕士生导师。主要从事医学图像处理、深度学习、图像分割与目标检测、多目标进化计算等方面的研究。先后主持国家自然科学基金面上项目、863项目子课题、浙江省自然科学基金以及多项企业横向课题。在国内外知名期刊和重要国际学术会议上发表论文40余篇，其中以第一作者或通讯作者在IEEE Transactions on Neural Networks and Learning Systems(TNNLS)、Pattern Recognition(PR)、IEEE/ACM Transactions on Computational Biology and Bioinformatics(TCBB)、Applied Soft Computing、Neural Computing and Applications、Neurocomputing等领域内的权威期刊和BIBM、ICANN、GECCO、IJCNN等CCF重要国际学术会议上发表论文30余篇，授权国家发明专利20余项。

• 先后主持国家自然科学基金面上项目、863项目子课题、浙江省自然科学基金以及多项企业横向课题。

  1、主持的纵向项目

  （1）中科院上海微系统与信息技术研究所基金项目：基于生物特征的步态识别算法研究（项目批准号：SYS201801），2019.10-2020.12，35万元（在研）

  （2）浙江省公益技术研究计划：面向临床的黑色素瘤初步筛查与智能诊断关键技术研究（项目批准号：LGF20H180002），2020.1-2022.12，10万元（在研）

  （3）浙江省自然科学基金：基于CNN 的相差显微图像序列下的癌细胞检测与多目标跟踪（项目批准号：Y18F030084），2018.1-2019.12，10万元（结题）

  （4）国家自然科学基金面上项目：具有区间输出约束的温室环境系统多变量优化控制算法的研究（项目批准号：61374094），2014.1-2017.12, 81万元（结题）

  （5）国家863子课题：多参数水质信息采集的数据无线传输技术研究（项目批准号：2008AA10Z227），2010.1-2011.12, 10万元（结题）

  2、主持的横向项目

    

  （1）企业（浙江蓝虹科技有限公司）横向项目，大数据驱动的固废资源循环利用综合评价系统，2020.5-2023.5，300万元（在研）

  （2）企业（东莞西尼自动化科技有限公司）横向项目，面向电子元器件检测的图像处理算法优化，2020.1-2021.1，15万元（在研）

  （3）企业（杭州创美实业）横向项目，三相智能型漏电保护器的研发，2013.3-2018.6，45万元（结题）

  （4）企业（浙江中力机械有限公司）横向项目，基于深度学习的焊缝质量检测与跟踪技术研发，2016.7-2017.6，10万（结题）

  
  
  

  
  

• 1. Selected journal papers

  [1]  Huihuang Zhang, Haigen Hu*, Deming Zhou, Xiaoqin Zhang, Bin Cao. Compact CNN module balancing between feature diversity and redundancy. Neural Networks, Vol.188,August 2025,107456.

  [2]     Haigen Hu, Huihuang Zhang, Qianwei Zhou and Tieming Chen. Pruning Networks Only Using Few-shot Pre-training Based on Gradient Similarity Frequency. IEEE Transactions on Artificial Intelligence, doi: 10.1109/TAI.2025.3544582, Feb. 2025.

  [3]  Haigen Hu, Chenghan Yu, Qianwei Zhou, Qiu Guan, Hailin Feng. HDConv: Heterogeneous kernel-based dilated convolutions. Neural Networks, 2024, Vol. 179, 106568.  Paper, Code

  [4] Haigen Hu, Aizhu Liu, Qiu Guan, Xiaoxin Li, Shengyong Chen, Qianwei Zhou. Adaptively Customizing Activation Functions for Various Layers. IEEE Transactions on Neural Networks and Learning Systems,2023, 34(9): 6096-6107. Paper, Code 

  [5]   Haigen Hu, Leizhao Shen, Qiu Guan, Xiaoxin Li, Qianwei Zhou, Su Ruan. Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images. Pattern Recognition, 2022, Vol. 124, 108452.  Paper, Code 

   [6]   Tian Qiu,Haigen Hu*, Ruixin Chen, Qianwei Zhou, Qiu Guan, Xiaoxin Li. A multi-root solver for discontinuous and non-differentiable equations by integrating genetic algorithm and derivative-free iterative methods, Applied Soft Computing, 2021, Vol.109, 107493. Paper
  

   [7]    Haigen Hu, Qiu Guan, Shengyong Chen*, Zhiwei Ji, Lin Yao. Detection and Recognition for Life State of Cell Cancer Using Two-Stage Cascade CNNs.IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, 17(3):887-898. Paper

  2. Selected conference papers

  [1]   Yujie Zhou, Haigen Hu*, Qianwei Zhou, Qiu Guan and Mingfeng Jiang. Rethinking Domain Generalization from Perspective of Gradient Granularity. 27th European Conference on Artificial Intelligence (ECAI-2024), Santiago de Compostela, 19-24 October, 2024.  Code

  [2]   Haigen Hu, Jingshan Hong, Kangkang Song, Jinwei Zhu and Weilun Ren. Cut-Stitch: a Simple and Effective Data Augmentation Method for Industrial Inspection. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), 9-13 September, 2024, Vilnius.  Code

  [3]   Kangkang Ai, Haigen Hu*, Qianwei Zhou and Qiu Guan. SGT: Self-Guided Transformer for Few-Shot Semantic Segmentation. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),Seoul, Korea, 14-19 April,2024, pp.5935-5939.

  [4]   Zhichao Jin, Haigen Hu*, Qianwei Zhou, Qiu Guan, Xiaoxin Li, and Qi Chen. Joint Feature Learning for Cell Segmentation Based on Multi-scale Convolutional U-Net. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, Nevada, USA, Dec. 6-8, 2022, pp.1580-1585. 

  [5]   Aizhu Liu, Haigen Hu*, Tian Qiu, Qianwei Zhou, Qiu Guan, Xiaoxin Li. Exploring Optimal Adaptive Activation Functions for Various Tasks. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), Dec. 16-19, 2020, pp.2290-2297. 

  [6]  Haigen Hu,Chao Du,Pierre Decazes, Pierre Vera, Su Ruan. A Prior Knowledge Integrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, November 18-21, 2019,pp.2413-2420. 

  [7]     Haigen Hu, Chao Du, Qiu Guan, Qianwei Zhou, Plerre Vera, Su Ruan. A Background-based Data Enhancement Method for Lymphoma Segmentation in 3D PET Images. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, November 18-21, 2019,pp. 1194-1196. 

  [8]   Haigen Hu, Yixing Zheng, Qianwei Zhou, Jie Xiao, Shengyong Chen, Qiu Guan. MC-Unet: Multi-scale Convolution Unet for Bladder Cancer Cell Segmentation in Phase-Contrast Microscopy Images. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, November 18-21, 2019,pp. 1197-1199. 

  
  

Currently, I mainly teach “big data development technology”, “big data basic computing framework”,  “computer engineering training” and other courses.