Publications

Journal Publication

• H. Niu, Z. Yi, and T. He*. “A bidirectional feedforward neural network architecture using the discretized neural memory ordinary differential equation.” International Journal of Neural Systems, 2024, doi: 10.1142/S0129065724500151.
• 邱韬，何涛，张强，肖雨璇，郭维华。基于人工智能技术的面相照片标志点自动定位的评价。口腔医学，2023年12月，第43卷，第12期。
• T. He, G. Xu, L. Cui, W, Tang, J. Long, and J. Guo, “Anchor ball regression model for large-scale 3d skul landmark detection”, Neurocomputing, vol 567, p.127051,2024.
• W. Dong, M. You, T. He, J. Dai, Y. Tang, Y. Shi, J. Guo, “An automatic methodology for full dentition maturity staging from OPG images using deep learning”, Applied Intelligence, pp. 1-23, 2023, doi: 10.1007/s10489-023-05096-0.
• 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, 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, vol. 33, no. 4, pp. 1740–1751, 2022, doi: 10.1109/TNNLS.2020.3043752.
• 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, 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, 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.

Conference Publication

• Q. He, T. He*, and Z. Yi, “Medical image segmentation using discretized nmODE” in Interational Amal Conference on Complex Systems andIntelligent Science, doi:10.1109/CSIS-IAC60628.2023.10363958.
• Y. Liu, L. Yang, T. Wang, J. Wu, T. He*, and Z. Yi, “An optimized yolo model with local atention for arthritis lesion detecion on x-rayimages” in lntemational Annual Conference on Complex Svstems and imtelligent science, 2023. doi: 10.1109CSIS-IAC60628.2023.10364047.
• Q. Zhang, J. Guo, T. He, J. Yao, W. Tang, and Z. Yi, “A novel landmark detection method for cephalometric measurement,” in 2021 IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE), 2021, pp. 1–10.
• 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.
• 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.