王进科(专硕)

发布者:董亮发布时间:2023-04-28浏览次数:1336

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

出生年月

1983.01

导师类别

专业学位硕导

技术职称

教授

任职部门

荣成学院

E_mail

19598985@qq.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

2018.09-2023-08,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,硕导

2023.09-至今,哈尔滨理工大学荣成学院,教授

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

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 Review2022.

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

  1. 2010,哈尔滨理工大学优秀教师

  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