何涛于2017年在四川大学计算机学院攻读博士学位，师从 章毅教授，IEEE Fellow，俄罗斯工程院外籍院士 。2017年于 四川大学智能医学中心 ，从事人工智能、医学图像、口腔智能医学等研究，主要在医学图像分割，医学图像关键点检测等相关领域开展研究。现就职于四川大学计算机学院，任副研究员。
Tao He is an associate researcher in colledge of computer science from Sichuan University, Chengdu, China, from 2017, surprivised by Prof. Zhang Yi, IEEE Fellow. His research interest includes Deep Learning, Medical Imaging, and Stomatology Intelligent Medical in Machine Intelligence Laboratory. Recently, he focus on medical image segmentation, medical image landmark detection, and etc.
T. He, J. Guo, W. Tang, W. Zeng, P. He, F. Zeng, and Z. Yi, “Cascade-refine model for cephalometric landmark detection in high-resolution orthodontic images,” .Knowledge-Based Systems, p. 110332, 2023, doi: 10.1016/j.knosys.2023.110332.
J. Yao, W. Zeng, T. He, S. Zhou, Y. Zhang, J. Guo, and W. Tang, “Automatic localization of cephalometric landmarks based on convolutional neural network,” American Journal of Orthodontics and Dentofacial Orthopedics, vol. 161, no. 3, pp. e250–e259, 2022, doi: 10.1016/j.ajodo.2021.09.012.
T. He, J. Yao, W. Tian, Z. Yi, W. Tang, and J. Guo, “Cephalometric landmark detection by considering translational invariance in the twostage framework,” Neurocomputing, vol. 464, pp. 15–26, 2021, doi: 10.1016/j.neucom.2021.08.042.
T. He, L. Zhang, J. Guo, and Z. Yi, “Multilabel classification by exploiting data-driven pair-wise label dependence,” International Journal of Intelligent Systems, vol. 35, no. 9, pp. 1375–1396, 2020, doi: 10.1002/int.22257.
T. He, H. Mao, and Z. Yi, “Subtraction gates: Another way to learn long-term dependencies in recurrent neural networks,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–12, 2020, doi:10.1109/TNNLS.2020.3043752.
T. He, J. Hu, Y. Song, J. Guo, and Z. Yi, “Multi-task learning for the segmentation of organs at risk with label dependence,” Medical Image Analysis, vol. 61, p. 101666, 2020, doi: 10.1016/j.media.2020.101666.
T. He, J. Guo, N. Chen, X. Xu, Z. Wang, K. Fu, L. Liu, and Z. Yi, “Medimlp: Using grad-cam to extract crucial variables for lung cancer postoperative complication prediction,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 6, pp. 1762–1771, 2020, doi: 10.1109/JBHI.2019.2949601.
T. He, J. Guo, J. Wang, X. Xu, and Z. Yi, “Multi-task learning for the segmentation of thoracic organs at risk in ct images.” in SegTHOR@ ISBI, 2019, pp. 10–13.
T. He, H. Mao, and Z. Yi, “Moving object recognition using multiview three-dimensional convolutional neural networks,” Neural computing and applications, vol. 28, no. 12, pp. 3827–3835, 2017, doi: 10.1007/s00521-016-2277-9.
T. He, H. Mao, J. Guo, and Z. Yi, “Cell tracking using deep neural networks with multi-task learning,” Image and Vision Computing, vol. 60, pp. 142–153, 2017, doi: 10.1016/j.imavis.2016.11.010.
B. Wu, J. Jia, T. He, J. Du, X. Yi, and Y. Ning, “Inferring users’ emotions for human-mobile voice dialogue applications,” in 2016 IEEE International Conference on Multimedia and Expo (ICME), 2016, pp. 1–6, doi: 10.1109/ICME.2016.7552890.
J. Jia, J. Huang, G. Shen, T. He, Z. Liu, H. Luan, and C. Yan, “Learning to appreciate the aesthetic effects of clothing,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1, 2016.
IEEE Transactions on Cybernetics (TCYB)
IEEE Transactions on Neural Network and Learning Systems (TNN)
IEEE Transactions on Medical Imaging (TMI)
IEEE Transactions on Image Processing (TIP)