TOHOKU UNIVERSITY Researchers

Research History 3

2025/04 - Present

Tohoku University　Graduate School of Infomation Sciences, Information Security　Assistant Professor
2024/03 - Present

Tohoku University　Center for Data-driven Science and Artificial Intelligence Division for Data Assets and Information Security　Assistant Professor
2023/11 - 2024/02

東北大学医学部医用画像工学分野　学術研究員

Education 3

Tohoku University　Graduate School of Biomedical Engineering

2018/10 - 2023/09
Tohoku University　Graduate School of Biomedical Engineering

2016/10 - 2018/09
Beihang University (BUAA)

2012/09 - 2016/06

Committee Memberships 1

計測自動制御学会システム・情報部門学術講演会（SSI2025）　現地実行委員（会場担当）

2025 - 2025

Professional Memberships 1

IEEE

2023/12 - Present

Research Interests 3

説明可能AI
医用画像診断支援システム
人工知能

Research Areas 2

Informatics / Perceptual information processing /
Life sciences / Medical systems /

Awards 2

The Best Paper Prize

2023/12　IEEE Sendai Section
SSI Excellent Paper Award

2018/11　SICE System and Information Division

Papers 11

Transformer-based Deep Learning Models with Shape Guidance for Predicting Breast Cancer in Mammography Images Peer-reviewed

Kengo Takahashi, Yuwen Zeng, Zhang Zhang, Kei Ichiji, Takuma Usuzaki, Ryusei Inamori, Haoyang Liu, Noriyasu Homma

Journal of Imaging Informatics in Medicine　2025/12/19
Publisher: Springer Science and Business Media LLC
DOI： 10.1007/s10278-025-01773-3 　

eISSN： 2948-2933

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Abstract Recent breast cancer research has investigated shape-based attention guidance in Vision Transformer (ViT) models, focusing on anatomical structures and the heterogeneity surrounding tumors. However, few studies have clarified the optimal transformer encoder layer stage for applying attention guidance. Our study aimed to evaluate the effectiveness of shape-guidance strategies by varying the combinations of encoder layers that guide attention to breast structures and by comparing the proposed models with conventional models. For the shape-guidance strategy, we applied breast masks to the attention mechanism to emphasize spatial dependencies and enhance the learning of positional relationships within breast anatomy. We then compared the representative models—Masked Transformer models that demonstrated the best performance across layer combinations—with the conventional ResNet50, ViT, and SwinT V2. In our study, a total of 2,436 publicly available mammography images from the Chinese Mammography Database via The Cancer Imaging Archive were analyzed. Three-fold cross-validation was employed, with a patient-wise split of 70% for training and 30% for validation. Model performance on differentiating breast cancer from non-cancer images was assessed by the area under the receiver-operating characteristic curve (AUROC). The results showed that applying masks at the Shallow and Deep stages gave the highest AUROC for Masked ViT. The Masked ViT achieved an AUROC of 0.885 [95% confidence interval: 0.849–0.918], a sensitivity of 0.876, and a specificity of 0.802, outperforming all other conventional models. These results indicate that incorporating mask guidance into particular Transformer encoders promotes representation learning, highlighting their potential as decision-support tools in breast cancer diagnosis.
Robust Ownership Verification of DNN Models Against JPEG Compression via Probability-Controlled Adversarial Attacks

Teruki Sano, Minoru Kuribayashi, Masao Sakai, Shuji Isobe, Eisuke Koizumi, Zhang Zhang

2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)　2140-2145　2025/10/22
Publisher: IEEE
DOI： 10.1109/apsipaasc65261.2025.11249003 　
Adaptive region-oriented masked vision retentive network for predicting macrovascular invasion in hepatocellular carcinoma Peer-reviewed

Kengo Takahashi, Ryusei Inamori, Kei Ichiji, Zhang Zhang, Zeng Yuwen, Noriyasu Homma

Medical Imaging 2025: Computer-Aided Diagnosis　65-65　2025/04/04
Publisher: SPIE
DOI： 10.1117/12.3045625 　
Bilateral Information-Guided Diagnosis of Breast Masses in Mammography Using Vision Transformer Peer-reviewed

Tianyu Zeng, Yuwen Zeng, Zhang Zhang, Xiaoyong Zhang, Kei Ichiji, Shuo-Yan Chou, Ivo Bukovsky, Jan Vrba, Noriyasu Homma

IEEE Journal of Biomedical and Health Informatics　1-13　2025
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
DOI： 10.1109/jbhi.2025.3626200 　

ISSN： 2168-2194

eISSN： 2168-2208
Vision Transformer-Based Breast Mass Diagnosis in Mammography Using Bilateral Information Peer-reviewed

Tianyu Zeng, Zhang Zhang, Yuwen Zeng, Xiaoyong Zhang, Kei Ichiji, Noriyasu Homma

2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)　147-152　2024/08/26
Publisher: IEEE
DOI： 10.1109/iicaiet62352.2024.10730097 　
Attention Optimization in AI-Aided Drowning Diagnosis Using Post-Mortem CT to Mitigate Overfitting with Limited Training Data Peer-reviewed

Zhang Zhang, Xiaoyong Zhang, Taihei Mizuno, Kei Ichiji, Noriyasu Homma

2024 International Joint Conference on Neural Networks (IJCNN)　32　1-6　2024/06/30
Publisher: IEEE
DOI： 10.1109/ijcnn60899.2024.10650327 　
How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images Peer-reviewed

Zhang Zhang, Xiaoyong Zhang, Kei Ichiji, Ivo Bukovský, Noriyasu Homma

Scientific Reports　13　(1)　2023/11/03
Publisher: Springer Science and Business Media LLC
DOI： 10.1038/s41598-023-45368-w 　

eISSN： 2045-2322

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Abstract Over the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods’ (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories. However, due to the ICI within each medical facility, medical data are often isolated and acquired in different settings among medical facilities, known as the issue of intra-source imbalance (ISI) characteristic. This imbalance also impacts the performance of DLMs but receives negligible attention. In this study, we study the impact of the ISI on DLMs by comparison of the version of a deep learning model that was trained separately by an intra-source imbalanced chest X-ray (CXR) dataset and an intra-source balanced CXR dataset for COVID-19 diagnosis. The finding is that using the intra-source imbalanced dataset causes a serious training bias, although the dataset has a good inter-category balance. In contrast, the deep learning model performed a reliable diagnosis when trained on the intra-source balanced dataset. Therefore, our study reports clear evidence that the intra-source balance is vital for training data to minimize the risk of poor performance of DLMs.
How Different Data Sources Impact Deep Learning Performance in COVID-19 Diagnosis using Chest X-ray Images Peer-reviewed

Zhang Zhang, Xiaoyong Zhang, Kei Ichiji, Ivo Bukovský, Shuoyan Chou, Noriyasu Homma

2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)　4　508-513　2023/07/08
Publisher: IEEE
DOI： 10.1109/iiai-aai59060.2023.00103 　
Risk Analysis of Breast Cancer by Using Bilateral Mammographic Density Differences: A Case-Control Study Peer-reviewed

Zhang Zhang, Xiaoyong Zhang, Jiaqi Chen, Yumi Takane, Satoru Yanagaki, Naoko Mori, Kei Ichiji, Katsuaki Kato, Mika Yanagaki, Akiko Ebata, Minoru Miyashita, Takanori Ishida, Noriyasu Homma

The Tohoku Journal of Experimental Medicine　261　(2)　139-150　2023
Publisher: Tohoku University Medical Press
DOI： 10.1620/tjem.2023.j066 　

ISSN： 0040-8727

eISSN： 1349-3329
Deep Learning-Based Diagnosis of Fatal Hypothermia Using Post-Mortem Computed Tomography Peer-reviewed

Yuwen Zeng, Xiaoyong Zhang, Issei Yoshizumi, Zhang Zhang, Taihei Mizuno, Shota Sakamoto, Yusuke Kawasumi, Akihito Usui, Kei Ichiji, Ivo Bukovsky, Masato Funayama, Noriyasu Homma

The Tohoku Journal of Experimental Medicine　260　(3)　253-261　2023
Publisher: Tohoku University Medical Press
DOI： 10.1620/tjem.2023.j041 　

ISSN： 0040-8727

eISSN： 1349-3329
Adaptive Gaussian Mixture Model-Based Statistical Feature Extraction for Computer-Aided Diagnosis of Micro-Calcification Clusters in Mammograms Peer-reviewed

Zhang Zhang, Xiaoyong Zhang, Kei Ichiji, Yumi Takane, Satoru Yanagaki, Yusuke Kawasumi, Tadashi Ishibashi, Noriyasu Homma

SICE Journal of Control, Measurement, and System Integration　13　(4)　183-190　2020/07/01
Publisher: Informa UK Limited
DOI： 10.9746/jcmsi.13.183 　

ISSN： 1882-4889

eISSN： 1884-9970

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Misc. 6

LLMを用いた放射線読影レポートの平易化における語彙頻度に基づく可読性評価の検討

曾昱雯, 三上真理子, 張彰, 張暁勇, 本間経康

2026/02
逆変形ベクトル場表現を用いた 3 次元体内動態の粒子フィルタに基づく推定

川浪佑介, 市地慶, 張彰, 本間経康

SSI2025　2025/11
High-Resolution Abnormal Mammogram Generation from Normal Samples Using Density-Aware Dual-Mask CycleGAN for Imbalanced Dataset Augmentation

Wuhong Jiang, Zhang Zhang, Yuwen Zeng, Kei Ichiji, Xiaoyong Zhang, Noriyasu Homma

SSI2025　2025/11
Adversarial domain adaptation for breast cancer diagnosis in multi-center datasets

Xinyang He, Yuwen Zeng, Zhang Zhang, Kei Ichiji, Xiaoyong Zhang, Noriyasu Homma

SSI2025　2025/11
深層学習は乳癌画像をどう読むか

本間経康, 本間経康, 張暁勇, 張暁勇, 高野寛己, 野呂恭平, 張彰, 陳家旗, 市地慶, 市地慶, 杉田典大, 酒井正夫, 吉澤誠, 川住祐介, 石橋忠司

日本乳癌画像研究会プログラム・抄録集　28th　2019
Computer-Aided Diagnosis of Micro-Calcification Clusters in Mammograms Using an Adaptive Gaussian Mixture Model

Zhang, Z, Zhang, X, Ichiji, K., Osanai, M., Homma, N.

SSI2018　2018/11

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Presentations 6

データの施設多様性が乳房腫瘤検出モデルの性能に与える影響の分析

菅野真梨子, 張彰, Yuwen Zeng, 市地慶, 張暁勇, 本間経康

第53回日本放射線技術学会秋季学術大会　2025
Particle filter-based 3D volumetric motion estimation by using local constant velocity model for improved target tracking

2024/12/17
Reliability investigation of deep learning when using imbalanced chest X-ray image sources for COVID-19 detection.

Zhang, Z, Zhang, X, Homma, N

2023 Tohoku-Section Joint Convention of Institutes of Electrical and Information Engineers　2023
How intra-source imbalance affects deep learning performance for COVID- 19 diagnosis using chest X-ray images

Zhang Zhang, Xiaoyong Zhang, Kei Ichiji, Ivo Bukovsky, Noriyasu Homma

International Symposium on Human Welfare Engineering for Smart-Aging　2022
Quantitative Evaluation of Explainable AI for COVID-19 Detection in Chest X-ray Images.

Zhang, Z, Zhang, X, Wang, J, Zeng, Y, Ichiji, K, Homma, N

FAN 2021 Online　2021
顕著性マップを用いた死後CTの溺死鑑別における深層学習モデルの性能向上の試み

水野泰平, 張曉勇, 張彰, 市地慶, 杉田典大, 本間経康

第31回インテリジェント・システム・シンポジウム

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