东北大学主页平台李赫--中文主页-- H. Li et al., "A Weakly-Supervised Anomaly Detection Method via Adversarial Training for Medical Images," 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2022, pp. 1-4, doi: 10.1109/ICCE53296.2022.9730129.

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李赫

个人信息Personal Information

博士生导师
硕士生导师
教师拼音名称：lihe
电子邮箱：
入职时间：2025-10-23
职务：特聘研究员/一级副教授
学历：博士研究生毕业
办公地点：信息学馆B616
性别：男
联系方式：
学位：博士
职称：副教授
在职信息：在职
毕业院校：立命馆大学
所属院系：软件学院

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论文成果

H. Li et al., "A Weakly-Supervised Anomaly Detection Method via Adversarial Training for Medical Images," 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2022, pp. 1-4, doi: 10.1109/ICCE53296.2022.9730129.

发布时间：2026-03-06 点击次数：

论文名称：H. Li et al., "A Weakly-Supervised Anomaly Detection Method via Adversarial Training for Medical Images," 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2022, pp. 1-4, doi: 10.1109/ICCE53296.2022.9730129.

是否译文：否

上一条：Li, H., Iwamoto, Y., Han, X., Lin, L., Hu, H., Chen, YW. (2022). An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-lesions. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_21

下一条：H. Li, Y. Iwamoto, X. Han, A. Furukawa, S. Kanasaki and Y. -W. Chen, "An Efficient and Accurate 3D Multiple-Contextual Semantic Segmentation Network for Medical Volumetric Images," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 3309-3312, doi: 10.1109/EMBC46164.2021.9629671.