哈尔滨理工大学自动化学院
研究生指导教师简介
| 姓 名 | 王进科 | 性 别 | 男 |
出生年月 | 1983.01 | 导师类别 | 专业学位硕士生导师 | |
技术职称 | 副教授 | 任职部门 | 荣成学院 | |
E_mail | ousinka@hotmail.com | |||
电 话 | 0631-7595512 | |||
教育经历 2002.09-2006.07山东大学，软件工程，工学学士 2006.09-2009.04山东大学，计算机应用，工学硕士 2011.03-2016.07哈尔滨工业大学，机械电子工程，工学博士 2017.09-2019.09大阪大学，放射线研究科，博士后 | ||||
工作经历 2009.07-2011.09哈尔滨理工大学荣成学院，助教 2011.09-2018.09哈尔滨理工大学荣成学院，讲师 2018.09-至今 哈尔滨理工大学荣成学院，副教授 2019.10-至今 哈尔滨理工大学自动化学院，硕士生导师 | ||||
研究领域及方向 1.研究领域（学科）： 电子信息、计算机应用 2. 主要研究方向： 医学图像处理、深度学习、机器学习 3. 个人简介 本人长期从事腹部CT、胸部CT、膝关节软骨MRI、眼底图像的医学图像处理与分析研究，欢迎自律自制，热衷AI的同学联系并加入本人所在的医学图像处理团队（https://402-lab.github.io/） | ||||
科研项目 1. 主持或参与纵向课题 [1]2018，主持国家自然科学基金，用于活体肝移植术前评估的肝脏CT图像自动分割方法研究 [2]2010，参与国家自然科学基金，用于准确诊断膝关节炎病情发展的MR图像配准研究 [3]2015，参与国家自然科学基金，MR图像监测骨关节炎病情变化的量化研究 [4]2019，主持黑龙江省自然科学基金，含病变肝脏CT的自动分割关键技术 [5]2016，主持黑龙江省自然科学基金，基MR图像精确配准技术监测膝关节软骨厚度变化 [6]2019，主持“理工英才”杰出青年人才项目，基于树模型与图谱模型的肝脏CT计算机辅助定位与分割技术 [7]2023，主持哈尔滨理工大学荣成学院拔尖创新团队培养计划，腹部CT的多器官自动分类识别关键技术 2. 主持横向课题 [1]2021，主持山东省荣成市核应急指挥中心委托项目，核应急演练管理平台 | ||||
代表性科研论文 [1]Wang J, Li X, Cheng Y. (2023) Towards an extended EfficientNet-based U-Net framework for joint optic disc and cup segmentation in the fundus image[J]. Biomedical Signal Processing and Control, 2023, 85: 104906. [2]Wang J, Zhou L, Yuan Z, et al.(2023) MIC-Net: multi-scale integrated context network for automatic retinal vessel segmentation in fundus image[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 6912-6931. [3]Wang J, Zhang X, Guo L, et al. Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT[J]. Mathematical biosciences and engineering: MBE, 2023, 20(1): 1297-1316. [4]Wang, J., Lv, P., Wang, H., & Shi, C. (2021). SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography. Computer Methods and Programs in Biomedicine, 208, 106268. [5]Lv, P., Wang, J., & Wang, H. (2022). 2.5 D lightweight RIU-Net for automatic liver and tumor segmentation from CT. Biomedical Signal Processing and Control, 75, 103567. [6]Lv, P., Wang, J., Zhang, X., & Shi, C. (2022). Deep supervision and atrous inception-based U-Net combining CRF for automatic liver segmentation from CT. Scientific Reports, 12(1), 16995. [7]Wang, J., Zhang, X., Lv, P., Wang, H., & Cheng, Y. (2022). Automatic liver segmentation using EfficientNet and Attention-based residual U-Net in CT. Journal of Digital Imaging, 1-15. [8]Lv, P., Wang, J., Zhang, X., Ji, C., Zhou, L., & Wang, H. (2022). An improved residual U-Net with morphological-based loss function for automatic liver segmentation in computed tomography. Math. Biosci. Eng, 19, 1426-1447. [9]Wang, J., Li, X., Lv, P., & Shi, C. (2021). SERR-U-Net: squeeze-and-excitation residual and recurrent block-based U-Net for automatic vessel segmentation in retinal image. Computational and Mathematical Methods in Medicine, 2021. [10]Jiang, J., Guo, Y., Bi, Z., Huang, Z., Yu, G., & Wang, J. (2022). Segmentation of prostate ultrasound images: the state of the art and the future directions of segmentation algorithms. Artificial Intelligence Review, 1-37. [11]Yao, D., Zhan, X., Zhan, X., Kwoh, C. K., Li, P., & Wang, J. (2020). A random forest based computational model for predicting novel lncRNA-disease associations. BMC bioinformatics, 21, 1-18. [12]Shi, C., Cheng, Y., Wang, J., Wang, Y., Mori, K., & Tamura, S. (2017). Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Medical image analysis, 38, 30-49. [13]Wang, J., Cheng, Y., Guo, C., Wang, Y., & Tamura, S. (2016). Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. International journal of computer assisted radiology and surgery, 11, 817-826. [14]Wang, J., & Guo, H. (2016). Automatic approach for lung segmentation with juxta-pleural nodules from thoracic CT based on contour tracing and correction. Computational and mathematical methods in medicine, 2016. [15]Wang, J., & Shi, C. (2017). Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy. Biomedical engineering online, 16, 1-19. [16]Guo, H., Song, S., Wang, J., Guo, M., Cheng, Y., Wang, Y., & Tamura, S. (2018). 3D surface voxel tracing corrector for accurate bone segmentation. International journal of computer assisted radiology and surgery, 13, 1549-1563. [17]Wang, J., Zu, H., Guo, H., Bi, R., Cheng, Y., & Tamura, S. (2019). Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT. Computer Assisted Surgery, 24, 20-26. [18]Guo, C., Cheng, Y., Guo, H., Wang, J., Wang, Y., & Tamura, S. (2015). Surface-based rigid registration using a global optimization algorithm for assessment of MRI knee cartilage thickness changes. Biomedical Signal Processing and Control, 18, 303-316. [19]Shi C, Cheng Y, Wang J, et al. Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation[J]. Medical image analysis, 2017, 38: 30-49. [20]Jiang, J., Guo, Y... Wang J, et al. Segmentation of prostate ultrasound images: the state of the art and the future directions of segmentation algorithms. Artificial Intelligence Review，2022. | ||||
代表性专利、专著 [1]王进科,杨志鹏. 一种用于人工智能图像的检测装置[P]. 黑龙江省：CN216483188U,2022-05-10. [2]王进科,孙艳霞. 一种医疗图像处理装置[P]. 黑龙江省：CN209059237U,2019-07-05. [3]王进科,晏清微. 一种医疗超声波诊断辅助装置[P]. 黑龙江省：CN209059262U,2019-07-05. | ||||
科研获奖 [1]2020，第二届荣成市青年科技奖 [2]2018，山东省高等学校科学技术三等奖 | ||||
社会、学会及学术兼职 《Biomedical Signal Processing and Control》、《International Journal of Computer Assisted Radiology and Surgery》、《Engineering Applications of Artificial Intelligence 》、《生物医学工程学报》等国内外期刊审稿人、ISIS系统评议专家。 | ||||
荣誉奖励 [2]2013，哈尔滨理工大学优秀毕业设计指导教师 [3]2016，哈尔滨理工大学优秀主讲教师 | ||||
