論文

• Deng Z., Yang Y., Suzuki K.. Federated Active Learning Framework for Efficient Annotation Strategy in Skin-Lesion Classification, Journal of Investigative Dermatology, vol. 145, no. 2, pp. 303-311, Feb. 2025.

著書

• Rahmaniar W, Deng Z., Yang Y., Jin Z., Suzuki K.. Decentralized Diagnostics: The Role of Federated Learning in Modern Medical Imaging, Advances in Intelligent Disease Diagnosis and Treatment, Springer, pp. 223-239, Sept. 2024.

国際会議発表 (査読有り)

• Deng Z., Jin Z., Suzuki K.. Dual-domain MTANN for virtual high-dose imaging in digital breast tomosynthesis (DBT), Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA 2024), Dec. 2024.
• Deng Z., Jin Z., Suzuki K.. Radiation Dose Reduction in Digital Breast Tomosynthesis by MTANN with Multi-scale Kernels, 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2023), July 2023.
• Zhipeng Deng, Ze Jin, Kenji Suzuki. Radiation Dose Reduction in Digital Breast Tomosynthesis (DBT) by Means of Multi-scale-Kernel Massive-Training Artificial Neural Network (MTANN) for Generating Virtual High-Dose Images, European Congress of Radiology (ECR 2023), Mar. 2023.
• Zhipeng Deng, Yuqiao Yang, Ze Jin, Kenji Suzuki. FedAL: An Federated Active Learning Framework for Efficient Labeling in Skin Lesion Analysis, International Conference on Systems, Man, and Cybernetics (IEEE SMC 2022), Oct. 2022.

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