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<!DOCTYPE html>
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<title>402Lab, HUST</title>
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<h1>王进科</h1>
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<p> 博士，教授，硕士生导师，哈尔滨工业大学毕业，大阪大学博士后出站，主要研究方向：医学图像处理，深度学习，机器学习。 </p>
<p> <strong>ORCID </strong>: <a href = "https://orcid.org/0000-0002-1755-4734/">https://orcid.org/0000-0002-1755-4734/</a></p>
<p>欢迎热衷AI的同学加入！<a href = "http://zidonghua.hrbust.edu.cn/4358/list.htm">自动化学院招生链接 </a><a href = "http://graduate.hrbust.edu.cn/3929/list.htm">研究生处招生链接</a></p>
<p> <strong> Email: </strong>[email protected] <strong> QQ: </strong>19598985 <strong>微信：</strong>jkwang2787</p>
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<!-- <p> 主要研究方向：医学图像处理，深度学习，机器学习</p>-->
<!-- <a href="/chn_resume/"><< English Version</a> -->
<h3>科研项目</h3>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[2019] 主持“理工英才”杰出青年，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.</p>
<p>[2017] 主持黑龙江省创新人才培养计划，CT图像中肝脏的自动分割方法研究.</p>
<p>[2017] 主持哈尔滨市科技创新人才，基于MR图像精准分割技术的膝关节软骨厚度监测研究.</p>
<p>[2016] 主持山东省高等学校科技计划，面向膝关节炎病情监测的医学图像分割与配准研究.</p>
<p>[2015] 主持黑龙江省教育厅科学技术研究项目，基于MR图像的膝关节软骨厚度监测技术研究.</p>
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<h3>论文成果</h3>
<p>[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.<p>
<p>[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.<p>
<p>[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.<p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.</p>
<p>[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.<p>
<p>[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.<p>
<p>[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.<p>
<p>[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.<p>
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