周同雪
一、导师基本信息
姓名： 周同雪
办公电话：
邮箱：txzhou@djbet88.net
指导专业：计算机科学与技术、电子信息
二、研究领域及方向
研究领域涵盖深度学习、图像处理、医学影像数据分析以及多模态数据融合。具体包括：运用深度神经网络技术解决图像分类、目标检测、视觉跟踪以及语义分割等问题；医学影像数据分析方面，针对各种医学成像技术（MRI、CT、PET）开发深度学习模型用于自动识别和标记潜在的病变、异常结构以及解剖特征。多模态数据融合方面，致力于整合来自不同传感器和数据源的信息，以提供更全面和准确的医学诊断和治疗方案。
三、主讲课程
计算机科学导论、Python程序设计
四、教育及工作经历
2022年毕业于法国鲁昂国立应用科学学院（INSA Rouen），获计算机科学专业博士学位；2017年—2018年在德国弗劳恩霍夫研究所（Fraunhofer Darmstadt）担任国家公派访问学者；现为杭州师范大学电子竞技博彩 讲师。
五、学术简介
主要研究领域为深度学习、图像分类、目标检测、视觉跟踪以及语义分割。主持国家自然科学青年基金项目1项， 2021年—2022年重点参与了欧洲区域发展基金项目（胶质母细胞瘤复发预测的算法研究）。 以第一作者在IEEE TIP、Pattern Recognition等顶级期刊以及MICCAI、ICPR等知名会议发表论文20余篇，谷歌学术论文引用量达1066次，单篇最高他引464次。
六、科研成果
【主持科研项目】
[1] 国家自然科学青年基金项目，深度学习指导下的缺失核磁共振模态图像的脑肿瘤分割与复发位置预测研究（62206084），2023.1-2025.12.
【近期发表期刊论文（2018-至今）】
[1] Zhou, T. (2023). Feature fusion and latent feature learning guided brain tumor segmentation and missing modality recovery network. Pattern Recognition, 141, 109665（SCI, 中科院一区, TOP期刊）
[2] Zhou, T., Ruan, S., Vera, P., & Canu, S. (2022). A tri-attention fusion guided multi-modal segmentation network. Pattern Recognition, 124, 108417 （SCI, 中科院一区, TOP期刊）
[3] Zhou, T., Canu, S., Vera, P., & Ruan, S. (2021). Latent correlation representation learning for brain tumor segmentation with missing MRI modalities. IEEE Transactions on Image Processing, 30, 4263–4274 （SCI, 中科院一区, TOP期刊）
[4] Zhou, T. (2023). Modality-level cross-connection and attentional feature fusion based deep neural network for multi-modal brain tumor segmentation. Biomedical Signal Processing and Control, 81, 104524.
[5] Zhou, T., Noeuveglise, A., Modzelewski, R., Ghazouani, F., Thureau, S., Fontanilles, M., & Ruan, S. (2023). Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning. Computerized Medical Imaging and Graphics, 106, 102218.
[6] Zhou, T., Ruan, S., & Hu, H. (2023). A literature survey of mr-based brain tumor segmentation with missing modalities. Computerized Medical Imaging and Graphics, 104, 102167.
[7] Zhou, T., & Zhu, S. (2023). Uncertainty quantification and attention-aware fusion guided multi-modal mr brain tumor segmentation. Computers in Biology and Medicine, 163, 107142.
[8] Zhou, T., Vera, P., Canu, S., & Ruan, S. (2022). Missing data imputation via conditional generator and correlation learning for multimodal brain tumor segmentation. Pattern Recognition Letters, 158, 125–132.
[9] Zhou, T., Canu, S., & Ruan, S. (2021). Automatic covid-19 ct segmentation using u-net integrated spatial and channel attention mechanism. International Journal of Imaging Systems and Technology, 31(1), 16–27.
[10] Zhou, T., Canu, S., Vera, P., & Ruan, S. (2021). Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing mr modalities. Neurocomputing.
[11] Zhou, T., Canu, S., & Ruan, S. (2020). Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation. Computerized Medical Imaging and Graphics, 86, 101811.
[12] Zhou, T., Ruan, S., & Canu, S. (2019). A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 3, 100004.
[13] Zhou, T., Zeng, D.-d., Zhu, M., & Kuijper, A. (2019). A template consensus method for visual tracking. Optoelectronics Letters, 15(1), 70–74.
[14] Zeng, D., Zhu, M., Xu, F., & Zhou, T. (2018). Extended scale invariant local binary pattern for background subtraction. IET Image Processing, 12(8), 1292–1302.
【近期发表会议论文（2018-至今）】
[1] Zhou, T., Noeuveglise, A., Ghazouani, F., Modzelewski, R., Thureau, S., Fontanilles, M., & Ruan, S. (2022). Prediction of brain tumor recurrence location based on kullback–leibler divergence and nonlinear correlation learning”, 26th international conference on pattern recognition (icpr). In 26th international conference on pattern recognition (icpr).
[2] Zhou, T., Canu, S., Vera, P., & Ruan, S. (2021). 3d medical multi-modal segmentation network guided by multi-source correlation constraint. In 2020 25th international conference on pattern recognition (icpr) (pp. 10243–10250). IEEE.
[3] Zhou, T., Canu, S., Vera, P., & Ruan, S. (2021). A dual supervision guided attentional network for multimodal mr brain tumor segmentation. In International conference on medical image and computer-aided diagnosis.
[4] Hu, H., Shen, L., Zhou, T., Decazes, P., Vera, P., & Ruan, S. (2020). Lymphoma segmentation in pet images based on multi-view and conv3d fusion strategy. In 2020 ieee 17th international symposium on biomedical imaging (isbi) (pp. 1197–1200). IEEE.
[5] Zhou, T., Canu, S., Vera, P., & Ruan, S. (2020). Brain tumor segmentation with missing modalities via latent multi-source correlation representation. In Medical image computing and computer assisted intervention – miccai 2020 (pp. 533–541). Springer, Cham.
[6] Zhou, T., Ruan, S., Guo, Y., & Canu, S. (2020). A multi-modality fusion network based on attention mechanism for brain tumor segmentation. In 2020 ieee 17th international symposium on biomedical imaging (isbi) (pp. 377–380). IEEE.
[7] Zhou, T., Ruan, S., Hu, H., & Canu, S. (2019). Deep learning model integrating dilated convolution and deep supervision for brain tumor segmentation in multi-parametric mri. In International workshop on machine learning in medical imaging (pp. 574–582). Springer
【专利】
[1] 朱明，周同雪. 一种目标跟踪方法及系统, 中国, ZL201710288110.6，2020.03.10，国家发明专利
[2] 朱明，周同雪. 一种动态背景下运动目标的检测方法及装置, 中国, ZL201710351870.7，2021.04.06，国家发明专利