姓名：武盼盼

性别：女

职称：讲师

研究领域：模式识别、医学图像处理、人工智能、智能信息处理

联系方式：pwu@tjnu.edu.cn

个人简历/教育经历：2017年于河北工业大学电子科学技术专业获得工学博士学位。2013年11月至2015年11月先后在美国维克森林大学医学图像工程部CT实验室、马萨诸塞大学洛厄尔分校影像与信息学实验室开展两年博士联合培养研究。

教授课程：《数据结构》、《数据结构实验》、《操作系统》、《操作系统实验》、《数字图像处理》、《人工智能导论》

主要科研/教学成果：主持完成国家自然科学基金青年项目1项，天津市教委科研计划项目1项。在国内外重要刊物及会议上发表学术论文20余篇，申请发明专利1项。

代表性论文：

[1] Wu P, Xu Y, Zhao Z*, Liu Z, Gao X, Ren L, Zhang Y, Guo R* and Yu H. Three dimensional segmentation of abdominal arteries and veins using vision transformers and domain adaptation. Physics in Medicine and Biology, 2026, 71(1).

[2] Wu P, Gao X, Yu H*. ISTNet: a multi-scale transformer-based architecture for malaria cell classification. Medical & Biological Engineering & Computing, 2026.

[3] Wu P, Liu Z, Zhao Z, Guo R*, Yu H*. LCPT: A Lightweight Cell Classification Method for Microscope Images Based on VicinityViT and Channel-Position Attention. Signal, Image and Video Processing, 2025, 19:1240.

[4] Wu P, An P, Zhao Z*, Guo R, Ma X, Qu Y, Xu Y and Yu H*. A mult-stage training and deep supervision based segmentation approach for 3D abdominal multi-organ segmentation. Journal of X-Ray Science and Technology, 2025, 0(0):1-14.  

[5] Wu P, Liu Z, Zhao Z*, Guo R*, Yu H. QLViT: A Lightweight Cell Classification Method for Microscope Images Based on MViTv2 and Linear Attention. Contemporary Mathematics, 2025, 19:1240.

[6] Wu P, Qu Y, Zhao Z*, Liu Z and Yu H*. FQ-Conv-ViT: A quantized convolutional vision transformer model for diabetic retinopathy classification. Signal, Image and Video Processing, 2025, 19: 680.

[7] Liu Z, Wu P*, Zhao Z, and Yu H*. ILViT: An Inception-Linear Attention-Based Lightweight Vision Transformer for Microscopic Cell Classification. Journal of Imaging, 2025,11(7): 219.

[8] Wu P, Qu Y, Zhao Z*, Cui Y, Xu Y, An P. An adaptive weighted ensemble learning network for diabetic retinopathy classification. Journal of X-Ray Science and Technology, 2024,32(2):285-301.

[9] An P, Xu Y, Wu P*. Attention mechanism-based deep supervision network for abdominal multi-organ segmentation. Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT, FLARE 2023. Lecture Notes in Computer Science. Springer, Cham. 2024,14544:319-332 (01 July 2024).

[10] Wu P, Sun X, Zhao Z*, Wang H*, Pan S, B. Schuller. Classification of lung nodules based on deep residual networks and migration learning. Computational Intelligence and Neuroscience, 2020, 2020: 1-10.

[11] Wu P, Xia K, Yu H*. Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition, Computer Methods and Programs in Biomedicine, 2016, 136: 97-106.

[12] Wu P, Xia K*, Yu H*. Relevance vector machine based pulmonary nodule classification, Journal of Medical Imaging and Health Informatics, 2016, 6(1): 163-169.

奖励情况:

2019年入选天津市“131”创新型人才培养工程第三层次人选。