Journal Paper

• Yang Y., Sato M., Jin Z., Suzuki K. Patch-based Deep-learning Model with Limited Training Dataset for Liver Tumor Segmentation in Contrast-enhanced Hepatic Computed Tomography, IEEE Access, IEEE, Vol. 13, pp. 86863-86873, May 2025.
• Rahmaniar W., Deng Z., Yang Y., Jin Z., Suzuki K.. Future of the Medical World: Collaborative Medical Imaging AI with Federated Learning, IEEE Consumer Electronics Magazine, Feb. 2025.
• 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.

International Conference (Reviewed)

• Yanhong he., Ou Y., Dai P., Yang Y., Jin Z., Suzuki K.:. Orientation-Consistent Patch Sampling Method Based on Centerline for Colon Segmentation in CT, Nov. 2025.
• Dai P., Ou Y., Yang Y., Jin Z., Suzuki K.. GoCa: Trustworthy Multi-Modal RAG with Explicit Thinking Distillation for Reliable Decision-Making in Med-LVLMs, The 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025), Sept. 2025.
• Zhang H., Yang Y., Jin Z., Suzuki K. Sequence-aware MTANN for Semantic Segmentation of Rare Cancer in Multisequence MRI with Small Data Training, 40th Computer Assisted Radiology and Surgery (CARS 2025), June 2025.

Domestic Conference (Reviewed)

• Tenma Morikawa, Yafei Ou, Yuqiao Yang, Ze Jin, Kenji Suzuki. Improving Federated Learning for Skin Lesion Classification with Weight Perturbation Optimization to the ETF Classifier, 第44回日本医用画像工学会大会 (JAMIT 2025), Aug. 2025.

International Conference (Not reviewed / Unknown)

• Zhang H., Rahmaniar W., Yang Y., Nakatani F., Miyake M., Suzuki K.. Sequence-Aware MTANN for Segmentation of Rare Soft-Tissue Sarcomas in Multi-Sequence MRI with Missing Sequences, Nov. 2025.

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