Book

• Ze Jin, Taiguang Yuan, Yukiko Tokuda, Yasuto Naoi, Noriyuki Tomiyama, Kenji Suzuki. Radiomics: Approach to Precision Medicine, Artificial Intelligence and Machine Learning for Healthcare, Springer, pp. 17-29, Sept. 2022.  Official location  

International Conference (Reviewed)

• 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.
• Yuqiao Yang, Ze Jin, Kenji Suzuki. Federated Tumor Segmentation with Patch-wise Deep Learning Model, 25th International Conference on Medical Image Computing and Computer Assisted InterventionInternational (MICCAI) Workshop on machine learning in medical imaging (MLMI), Sept. 2022.
• Yuqiao Yang, Ze Jin, Kenji Suzuki. Federated Learning Coupled with Massive-Training Artificial Neural Networks in Tumor Segmentation in CT Images., The 44th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022), July 2022.

Patent

• Kenji Suzuki, Ze Jin. 画像処理装置、画像処理方法及びプログラムが格納された非一時的なコンピュータ可読媒体. Patent. Published. 国立大学法人東京工業大学. 2022/03/02. PCT/JP2022/008813. 2022/09/09. WO 2022/186263. 2022.

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