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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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.
