王蓉芳
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王蓉芳，西安电子科技大学人工智能学院副教授，博士学历，硕士生导师。2015年11月入职西安电子科技大学；2018年8月至2020年8月，前往美国德州大学西南医学中心交流访问。现为西安电子科技大学智能感知与图像理解教育部重点实验室成员，美国电气电子工程师协会(IEEE)会员，中国电子学会会员。
主要研究方向为机器学习，计算机视觉，智能遥感影像分析与解译，智能医学信息处理。通过不断学习和积累，在国际权威期刊/会议发表论文30余篇，已授权国家发明专利20余项。主持国家自然科学基金面上项目一项；主持陕西省自然科学基金面上项目一项；主持中科院国家重点实验室开放课题一项；主持武汉大学国家重点实验室开放课题一项；主持吉林大学等高校开放课题三项；主持与广东省惠州市企业合作的横向项目一项。作为主要参与人，参与国家重大项目“973”计划项目子课题一项，参与陕西省重点计划项目两项。
1. 机器学习
2. 计算机视觉
3. 智能遥感影像分析与解译
4. 智能医学信息处理
硕士研究生招生专业：
081200 计算机科学与技术
085404 计算机技术（专业学位）
085410 人工智能 （专业学位）
（原来的085400电子信息 更改为 085404 和 085410 两个专业）
面向西安电子科技大学本部招生；
优先考虑编程能力强、学习主动性高、英语和数学功底好的学生；
欢迎校内、外保研，以及统考学生报考或咨询。
欢迎有意向的推免生邮件联系，联系邮箱：rfwang@xidian.edu.cn
Q1：西电人工智能学院硕士研究生有哪些专业？
Q2：西电人工智能学院的推免政策和流程是怎样的呢？
Q3：西电人工智能学院的招生咨询联系方式是什么呢？
论文成果
[1] Zhang S, Li W, Wang R, et al. DaliWS: A High-Resolution Dataset with Precise Annotations for Water Segmentation in Synthetic Aperture Radar Images[J]. Remote Sensing, 2024, 16(4): 720.[pdf]
[2] Wang R, Zhang C, Chen C, et al. A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing[J]. Remote Sensing, 2024, 16(2): 419.[pdf] [codes][data]
[3] Wang R, Liu H, Zhou Z, et al. ASF-LKUNet: Adjacent-Scale Fusion U-Net with Large-kernel for Medical Image Segmentation[J]. Authorea Preprints, 2023.[pdf][codes]
[4] Wang R, Wei H, Wang A, et al. Robust road detection on high-resolution remote sensing images with occlusion by a dual-decoded UNet[C]//IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023: 5716-5719.[pdf]
[5] Wang X, Chen J W, Wang R, et al. A Lite-CNN for Landslides Recognition on Remote Sensing Images Via Structure Pruning[C]//IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023: 6526-6529.[pdf]
[6] Li C, Wang R, Chen J W, et al. A Siamese Network for Semantic Change Detection Based on Multiscale Context Fusion[C]//IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023: 6648-6651.[pdf]
[7] Li W, Kong Y, Wang R, et al. Lightweight Landslide Detection Method Based On Depth Separable Convolution And Double Self-Attention Mechanism[C]//IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023: 6198-6201.[pdf]
[8] Li W, Zhang S, Wang R, et al. Construction and Analysis of Dali Water Segmentation Dataset of SAR Images[C]//IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023: 7070-7073.[pdf]
[9] Zhang C, Wang R, Chen J W, et al. A Multi-Branch U-Net for Water Area Segmentation with Multi-Modality Remote Sensing Images[C]//IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023: 5443-5446.[pdf]
[10] Wang R, Guo J, Zhou Z, et al. Locoregional recurrence prediction in head and neck cancer based on multi-modality and multi-view feature expansion[J]. Physics in Medicine & Biology, 2022, 67(12): 125004. [pdf] [codes]
[11] Wang R, Wang L, Wei X, et al. Dynamic graph-level neural network for SAR image change detection[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5. [pdf] [data] [codes]
[12] Guo J, Wang R, Zhou Z, et al. Multi-Modality and Multi-View 2D CNN to Predict Locoregional Recurrence in Head & Neck Cancer[C]//2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021: 1-7. [pdf]
[13] Wang R, Wang L, Dong P, et al. Graph-level neural network for SAR image change detection[C]//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021: 3785-3788. [pdf]
[14] Wang R, Wang W, Dong P, et al. Sar Image Change Detection via a Few-Shot Learning-Based Neural Network[C]//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021: 5287-5290. [pdf]
[15] Chen, L., Dohopolski, M., Zhou, Z., Wang, K., Wang, R., Sher, D. J., & Wang, J. (2020). Segmentation Guided Classification Scheme for Lymph Node Malignancy Prediction in Head and Neck Cancer. International Journal of Radiation Oncology, Biology, Physics, 108(3), e841.
[16] Wang, K., Zhou, Z., Chen, L., Wang, R., Sher, D., & Wang, J. (2020, June). Head Neck Cancer Locoregional Recurrence Prediction Using Delta-Radiomics Feature. In MEDICAL PHYSICS (Vol. 47, No. 6, pp. E622-E623). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY. [pdf]
[17] Wang, R., Zhang, Y., Pachnis, P., Vu, H., Wang, K., Deberardinis, R., & Wang, J. (2020, June). Deciphering Metabolic Features to Target Neuroblastoma Using Machine Learning. In MEDICAL PHYSICS (Vol. 47, No. 6, pp. E356-E357). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.
[18] Zhiguo Zhou, Rongfang Wang, Jing Yang, Rongbin Xu, and Jinkun Guo. Multimodal weighted network for 3D brain tumor segmentation in MRI images. InMedical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging 2021 Feb 15 (Vol. 11600, p. 116001O). International Society for Optics and Photonics. [pdf]
[19] Liyuan Chen, Michael Dohopolski, Zhiguo Zhou, Kai Wang, Rongfang Wang, David Sher, and Jing Wang. Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer. International Journal of Radiation Oncology* Biology* Physics. 2021. [pdf]
[20] Kai Wang, Zhiguo Zhou, Rongfang Wang, Liyuan Chen, Qiongwen Zhang, David Sher and Jing Wang*. A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer. Medical physics, 2020, 47(10):5392-400. [pdf]
[21] Rongfang Wang, Yuanyuan Zhang, Panayotis Pachnis, Hieu Vu, Kai Wang, Ralph DeBerardinis and Jing Wang. Deciphering metabolic vulnerabilities to target neuroblastoma using machine learning methods. American Association of Physicists in Medicine Annual Meeting, July 12-16, 2020. Virtual meeting, United States. ( Oral presentation )
[22] Jing Yang, Rongfang Wang*, Yaochung Weng, Liyuan Chen, Zhiguo Zhou. A Hierarchical 3D U-Net for Brain Tumor Substructure Segmentation. American Association of Physicists in Medicine Annual Meeting, July 12-16, 2020. Virtual meeting, United States. ( eposter presentation ) [pdf]
[23] Rongfang Wang, Fan Ding, Jiawei Chen, Bo Liu, Jie Zhang, and Licheng Jiao. SAR Image Change Detection Method via a Pyramid Pooling Convolutional Neural Network. InIGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2020, 312-315. [pdf]
[24] Rongfang Wang, Weidong Wang, Jiawei Chen, Licheng Jiao, and Hongxia Hao. A Deep Generalized Correlation Network for Bitemporal Image Change Detection. InIGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2020, 2547-2550. [pdf]
[25] Rongfang Wang, Fan Ding, Jiawei Chen, Licheng Jiao, and Ling Wang. A Lightweight Convolutional Neural Network for Bitemporal Image Change Detection. InIGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2020, 2551-2554. [pdf]
[26] Rongfang Wang, Fan Ding, Licheng Jiao, Jiawei Chen*, Bo Liu, Wenping Ma, and Mi Wang. Lightweight convolutional neural network for bitemporal SAR image change detection. Journal of Applied Remote Sensing, 2020, 14(3):036501. [pdf]
[27] Jiawei Chen, Rongfang Wang*, Fan Ding, Bo Liu, Licheng Jiao, Jie Zhang. A convolutional neural network with parallel multi-scale spatial pooling to detect temporal changes in SAR images. Remote Sensing, 2020, 12(10):1619. [pdf]
[28] Rongfang Wang, Yaochung Weng, Zhiguo Zhou, Liyuan Chen, Hongxia Hao and Jing Wang. Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy. Physics in Medicine & Biology, 2019, 64:245005.1-14 [pdf]
[29] Shasha Mao, Jiawei Chen, Licheng Jiao, Shuiping Gou and Rongfang Wang. Maximizing diversity by transformed ensemble learning. Applied Soft Computing. 2019, 82:105580. 1-10.
[30] Rongfang Wang*, Y Weng, Z Zhou, L Chen, J Wang. Multiobjective Ensemble Deep Learning for Predicting Outcome After Lung Cancer Radiotherapy Using Electronic Health Records. Medical physics, 2019, 46(6): E283-E284.
[31] Rongfang Wang, Jiawei Chen, Yule Wang, Licheng Jiao and Mi Wang. SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance. IEEE Geoscience and Remote Sensing Letters, 2019, 17(1):77-81. [pdf]
[32] Rongfang Wang, Jiawei Chen*, Licheng Jiao and Mi Wang. How Can Despeckling and Structural Features Benefit to Change Detection on Bitemporal SAR Images? Remote Sensing, 2019, 11(4):421-440. [pdf]
[33] Rongfang Wang, Jie Zhang, Jiawei Chen, Licheng Jiao, and Mi Wang. Imbalanced Learning-Based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net. IEEE Geoscience and Remote Sensing Letters, 2019, 16(4):554-558. [pdf]
[34] Feng, J., Liu, L., Zhang, X., Wang, R., & Liu, H. (2017, July). Hyperspectral image classification based on stacked marginal discriminative autoencoder. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3668-3671). IEEE.
[35] Jing Gu*, Licheng Jiao, Fang Liu, Shuyuan Yang, Rongfang Wang, Puhua Chen, Yuanhao Cui, Junhu Xie, and Yake Zhang. Random subspace based ensemble sparse representation. Pattern Recognition, 2018, 74:544-55.
[36] Yifei Sun, Licheng Jiao, Xiaozheng Deng, and Rongfang Wang. Dynamic network structured immune particle swarm optimisation with small-world topology. International Journal of Bio-Inspired Computation, 2017, 9(2):93-105.
[37] Hongying Liu, Shuyuan Yang*, Shuiping Gou, Dexiang Zhu, Rongfang Wang, and Licheng Jiao. Polarimetric SAR feature extraction with neighborhood preservation-based deep learning, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4): 1456-1466.
[38] Hongying Liu, Shuang Wang, Rongfang Wang, Junfei Shi, Erlei Zhang, Shuyuan Yang, and Licheng Jiao. A Framework for Classification of Urban Areas Using Polarimetric SAR Images Integrating Color Features and Statistical Model. Journal of Infrared and Millimeter Waves, 2016, 35(4):398-406.
[39] Rongfang Wang, Licheng Jiao, Fang Liu, and Shuyuan Yang. Block-based adaptive compressed sensing of image using texture information. Acta Electronica Sinica, 2013, 41(8):1506-1514.
[40] Rongfang Wang, Licheng Jiao, Fang Liu, and Shuyuan Yang. Nature computation with self-adaptive dynamic control strategy of population size. Ruanjian Xuebao/Journal of Software, 2012, 23(7): 1760-1772.
2008.9 -- 2014.12
西安电子科技大学
电路与系统
博士研究生
工学博士学位
2004.9 -- 2007.3
西安电子科技大学
电路与系统
硕士研究生
工学硕士学位
2018.1 -- 至今
西安电子科技大学 人工智能学院 讲师 —> 副教授
2018.8 -- 2020.8
美国德州大学西南医学中心 放射肿瘤系 访问学者
2015.12 -- 2021.2
西安电子科技大学 电子工程学院 博士后 控制科学与工程、模式识别与智能系统专业
2015.11 -- 2017.12
西安电子科技大学 电子工程学院 讲师
