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JINZE 研究業績一覧 (51件)
論文
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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.
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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.
著書
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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.
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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.
公式リンク
国際会議発表 (査読有り)
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Zu J.,
Jin Z.,
Rahmaniar W.,
Suzuki K..
Synthesizing Virtual High-Dose Images from Low-Dose Images Using DD-MNet with Dual-Domain Denoising and Detail Reconstruction in Digital Mammography,
Nov. 2025.
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Beltaief F.,
Rahmaniar W.,
Jin Z.,
Suzuki K..
Knowledge Distillation for Lesion Detection and Classification on DBT for Limited Datasets Using Deep Learning,
Nov. 2025.
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Kodera S.,
Chavoshian S.M.,
Oshibe H.,
Jin Z.,
Suzuki K..
Difficulty-Based Active Boosting for Robust Lung Nodule Classification with Multi-Expert MTANN Ensemble,
Nov. 2025.
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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.
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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.
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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.
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Yuan T.,
Jin Z.,
Tokuda Y.,
Tomiyama N.,
Naoi Y.,
Suzuki K..
Forecast of genetic assessments for tumor response to chemotherapy only with pretherapeutic breast MRI by means of radiogenomic imaging biomarker scheme,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA 2024),
Dec. 2024.
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Qu T.,
Yang Y.,
Jin Z.,
Suzuki K..
Annotation-free AI learning of lung nodule segmentation in CT using weakly-supervised Massive -training Artificial neural networks,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA 2024),
Dec. 2024.
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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.
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Zhang C.,
Jin Z.,
Hori M.,
Sofue K.,
Murakami T.,
Suzuki K..
AI-aided diagnostic system providing explanations in LI-RADS language in liver cancer diagnosis using MRI,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA 2024),
Dec. 2024.
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Kodera S.,
Mohammad C.S.,
Jin Z.,
Watadani T.,
Abe O.,
Suzuki K..
Super-efficient AI for lung nodule classification in CT based on small-data massive-training artificial neural network (MTANN),
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA 2024),
Dec. 2024.
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Yuqiao Yang,
Ze Jin,
Fumihiko Nakatani,
Mototaka Miyake,
Kenji Suzuki.
“Small-data” Patch-wise Multi-dimensional Output Deep-learning for Rare Cancer Diagnosis in MRI under Limited Sample-size Situation,
21st IEEE International Symposium on Biomedical Imaging (ISBI 2024),
May 2024.
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Kodera S.,
Rahmaniar W.,
Oshibe H.,
Jin Z.,
Watadani T.,
Abe O.,
Suzuki K..
Super-Efficient Lung Nodule Classification Using Massive-Training Artificial Neural Network (MTANN) Compact Model on LIDC-IDRI Database,
2024 6th International Conference on Image, Video and Signal Processing (IVSP 2024),
Mar. 2024.
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Yang S.,
Xiang M.,
Qu T.,
Jin Z.,
Suzuki K..
Reconstruction of Fast Acquisition MRI with Under-sampled K-space Data by Using Massive-Training Artificial Neural Networks (MTANNs),
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2023.
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Yang Y.,
Jin Z.,
Nakatani F.,
Miyake M.,
Suzuki K..
Development of a small-data deep-learning model based on an MTANN for soft tissue sarcoma diagnosis in MRI,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2023.
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Jin Z.,
Pang M.,
Qu T.,
Oshibe H.,
Sasage R.,
Suzuki K..
Feature Map Visualization for Explaining Black-Box Deep Learning Model in Liver Tumor Segmentation,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2023.
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Yang Y.,
Jin Z.,
Suzuki K..
Federated learning - Game changing AI concept to train AI without sending patient data out from hospitals,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2023.
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Ze Jin,
Maolin Pang,
Yuqiao Yang,
Fahad Parvez Mahdi,
Tianyi Qu,
Ren Sasage,
Kenji Suzuki.
Explaining Massive-Training Artificial Neural Networks in Medical Image Analysis Task Through Visualizing Functions Within the Models,
The 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023),
Lecture notes in computer science, LNCS,
Oct. 2023.
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Pang M.,
Jin Z.,
Qu T.,
Mahdi F. P.,
Sasage R.,
Suzuki K..
Functional Model Visualization for Explaining Massive-Training Artificial Neural Network for Liver Tumor Segmentation,
45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC 2023),
July 2023.
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Yang S.,
Xiang M.,
Qu T.,
Jin Z.,
Suzuki K..
Under-sampled Image Reconstruction in Fast Acquisition MRI with Massive-Training Artificial Neural Networks (MTANNs) Deep Learning Approach,
45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2023),
July 2023.
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Yang Y.,
Jin Z.,
Nakatani F.,
Miyake M.,
Suzuki K..
AI-aided Diagnosis of Rare Soft-Tissue Sarcoma by Means of Massive-Training Artificial Neural Network (MTANN),
45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2023),
July 2023.
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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.
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Xu L.,
Mahdi F. P.,
Jin Z.,
Noguchi Y.,
Murata M.,
Suzuki K..
Generating simulated fluorescence images for enhancing proteins from optical microscopy images of cells using massive-training artificial neural networks,
SPIE Medical Imaging (SPIE MI),
Proceedings of SPIE,
Apr. 2023.
公式リンク
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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.
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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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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.
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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.
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Onai Y.,
Mahdi F. P.,
Jin Z.,
Suzuki K..
Virtual High-Radiation-Dose Image Generation from Low-Radiation-Dose Image in Digital Breast Tomosynthesis (DBT) Using Massive-Training Artificial Neural Network (MTANN),
The 6th International Symposium on Biomedical Engineering (ISBE2021),
Dec. 2021.
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Sato M.,
Yang Y.,
Jin Z.,
Suzuki K..
Segmentation of Liver Tumor in Hepatic CT by Using MTANN Deep Learning with Small Training Dataset Size,
The 6th International Symposium on Biomedical Engineering (ISBE2021),
Dec. 2021.
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Xiang M.,
Jin Z.,
Suzuki K..
Massive-Training Artificial Neural Network (MTANN) for Image Quality Improving in Fast-Acquisition MRI of the Knee,
The 6th International Symposium on Biomedical Engineering (ISBE2021),
Dec. 2021.
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Xiang M.,
Jin Z.,
Suzuki K..
Massive-Training Artificial Neural Network (MTANN) with Special Kernel for Artifact Reduction In Fast-Acquisition MRI of the Knee,
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI),
pp. 1210-1213,
May 2021.
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Sato M.,
Jin Z.,
Suzuki K..
Semantic Segmentation of Liver Tumor in Contrast-enhanced Hepatic CT by Using Deep Learning with Hessian-based Enhancer with Small Training Dataset Size,
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI),
pp. 34-37,
May 2021.
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Sato M.,
Jin Z.,
Suzuki K..
Small-Training-Set Deep Learning for Semantic Segmentation of Liver Tumors in Contrast-enhanced Hepatic CT,
European Congress of Radiology 2021,
Mar. 2021.
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Onai Y.,
Jin Z.,
Suzuki K..
Generation of Virtual High-Radiation-Dose Images from Low-Dose Images in Digital Breast Tomosynthesis (DBT) with Massive-Training Artificial Neural Network (MTANN),
European Congress of Radiology 2021,
Mar. 2021.
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Xiang M.,
Jin Z.,
Suzuki K..
Fast Acquisition MRI of the Knee by Means of Massive-Training Artificial Neural Network (MTANN) with Special Kernel,
European Congress of Radiology 2021,
Mar. 2021.
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Taiguang Yuan,
Ze Jin,
Yukiko Tokuda,
Yasuto Naoi,
Noriyuki Tomiyama,
Takashi Obi,
Kenji Suzuki.
Prediction of Genetically-Evaluated Tumour Responses to Chemotherapy from Breast MRI using Machine Learning with Model Selection,
Journal of Physics: Conference Series,
vol. 1780,
Oct. 2020.
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Taiguang Yuan,
Ze Jin,
Tokuda Y.,
Naoi Y.,
Tomiyama N.,
Kenji Suzuki.
MR Imaging biomarkers for Prediction of Genetic Assessment for Breast Cancer Recurrence: A Radiogenomics Study,
IEICE Technical Committee on Medical Imaging (MI),
IEICE Technical Report,
pp. 227-230,
Jan. 2020.
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Yuchen Wang,
Ze Jin,
Tokuda Y.,
Naoi Y.,
Tomiyama N.,
Kenji Suzuki.
Neural Network Convolution Deep Learning for Semantic Segmentation of Breast Tumor in MRI,
The 4th International Symposium on Biomedical Engineering (ISBE2019),
Proceeding of 4th International Symposium on Biomedical Engineering (ISBE2019),
pp. 286-287,
Nov. 2019.
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Taiguang Yuan,
Ze Jin,
Tokuda Y.,
Naoi Y.,
Tomiyama N.,
Kenji Suzuki.
Discovery of MR Imaging Biomarkers for Prediction of Pathological Complete Responses to Chemotherapy for Breast Cancer,
The 4th International Symposium on Biomedical Engineering (ISBE2019),
Proceeding of 4th International Symposium on Biomedical Engineering (ISBE2019),
Nov. 2019.
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Taiguang Yuan,
Ze Jin,
Tokuda, Y.,
Naoi, Y.,
Tomiyama, N.,
Kenji Suzuki.
Development of deep-learning segmentation for breast cancer in mr images based on neural network convolution,
2019 8th International Conference on Computing and Pattern Recognition,
Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition,
pp. 187-191,
Oct. 2019.
公式リンク
国内会議発表 (査読有り)
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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.
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XU YAFANGZHOU,
JIN ZE,
Sudo T.,
Watanabe H.,
鈴木 賢治.
MTANN を用いた AI イメージングによる歯科用コーンビーム CT における放射線被ばく低減手法の開発,
第44回日本医用画像工学会大会 (JAMIT 2025),
Aug. 2025.
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Chenggeer Li,
JIN ZE,
Oshibe H.,
竹下 尋紳,
Waki T.,
Ibara T.,
Fujita K.,
鈴木 賢治.
スモールデータ MTANN による X 線画像上の舟状骨骨折自動検出システムの開発,
第44回日本医用画像工学会大会 (JAMIT 2025),
Aug. 2025.
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染谷健太郎,
小寺昇冴,
押部弘子,
靳泽,
鈴木賢治.
CTにおける複数のMTANNを組み合わせた肺結節のセグメンテーション,
メディカルイメージング連合フォーラム2024,
Mar. 2024.
国内会議発表 (査読なし・不明)
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