Takuma Usuzaki, Eriya Matsuno, Takashi Shizukuishi, et al., “Multimodal deep learning for predicting WHO/ISUP grading in renal tumors on CT using a self-attention-based model: variable Vision Transformer (vViT)”, European Journal of Radiology Open, vol.17, Jun 2026.
Eichi Takaya, and Ryusei Inamori, “ModAn-MulSupCon: Modality- and anatomy-aware multi-label supervised contrastive pretraining for medical imaging”, Computer Methods and Programs in Biomedicine Update, Jun 2026.
Takuma Usuzaki, Kengo Takahashi, Hidenobu Takagi, et al., “Performance of a self-attention-based model in the task of differentiating clear cell renal cell carcinoma from other renal tumors: variable Vision Transformer (vViT)”, British Journal of Radiology, May 2026.
Ryusei Inamori, Tomoya Kobayashi, Eichi Takaya, et al., “Osteoporosis prediction from Frontal Lumbar Spine X-rays”, J Clin Densitom, vol.29, Issue 1, Jan 2026.
Kengo Takahashi, Yuwen Zeng, Zhang Zhang, et al., “Transformer-based Deep Learning Models with Shape Guidance for Predicting Breast Cancer in Mammography Images”, J Imaging Inform Med, Dec 2025.
Yuma Konaka, Takuya Ueda, Ryusei Inamori, et al., “Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of experts”, PLOS One, Sep 2025.
Keisuke Sugawara, Eichi Takaya, Ryusei Inamori, et al., “Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning”, Radiol Phys Technol, vol.18, pages 209-218, Jan 2025.
Tomoya Kobayashi, Junji Mochizuki, Kazuya Tashiro, et al., “Usefulness of fat attenuation index in postmortem CT for identifying responsible vessels in acute coronary syndromes: A case report”, Radiology Case Reports, vol.19, pages 5384-5388, Nov 2024.
Ryusei Inamori, Tomofumi Kaneno, Ken Oba, et al., “Impact of Downsampling Size and Interpretation Methods on Diagnostic Accuracy in Deep Learning Model for Breast Cancer Using Digital Breast Tomosynthesis Images”, The Tohoku Journal of Experimental Medicine, vol.265, pages 1-6, Jul 2024.
Kengo Takahashi, Takuma Usuzaki, and Ryusei Inamori, “Vision Transformer–based Deep Learning Models Accelerate Further Research for Predicting Neurosurgical Intervention”, Radiol Artif Intell, vol.6, no.4, Jun 12 2024.
Takuma Usuzaki, Ryusei Inamori, Mami Ishikuro, et al., “Predicting EGFR Status After Radical Nephrectomy or Partial Nephrectomy for Renal Cell Carcinoma on CT Using a Self-attention-based Model: Variable Vision Transformer (vViT)”, J Imaging Inform Med, vol.37, pages 3057-3069, Jun 2024.
Takuma Usuzaki, Ryusei Inamori, Takashi Shizukuishi, et al., “Predicting isocitrate dehydrogenase status among adult patients with diffuse glioma using patient characteristics, radiomic features, and magnetic resonance imaging: Multi-modal analysis by variable vision transformer”, Magn Reson Imaging, vol.111, pages 266-276, May 2024.
Takuma Usuzaki, Kengo Takahashi, Ryusei Inamori, et al., “Grading diffuse glioma based on 2021 WHO grade using self-attention-base deep learning architecture: variable Vision Transformer (vViT)”, Biomedical Signal Processing and Control, vol.91, May 2024.
Takuma Usuzaki, Kengo Takahashi, Ryusei Inamori, et al., “Identifying key factors for predicting O6-Methylguanine-DNA methyltransferase status in adult patients with diffuse glioma: a multimodal analysis of demographics, radiomics, and MRI by variable Vision Transformer”, Neuroradiology, vol.66, pages 761-773, Mar 2024.
Takuma Usuzaki, Kengo Takahashi, and Ryusei Inamori, “Be Careful About Metrics When Imbalanced Data Is Used for a Deep Learning Model”, CHEST, vol.165, Issue 3, pages e87-e89, Mar 2024.
Takuma Usuzaki, Kengo Takahashi, and Ryusei Inamori, “Letter to the editor on “Automated classification of fat-infiltrated axillary lymph nodes on screening mammograms””, British Journal of Radiology, vol.97, Issue 1154, pages 479-480, Feb 2024.
Kengo Takahashi, Takuma Usuzaki, and Ryusei Inamori, “Transformer Unlocks the Gateway to Advanced Research: Predicting Diseases on Chest Radiographs Using Multimodal Data”, Radiology, vol.310, no.2, Feb 13 2024.
Kengo Takahashi, Takuma Usuzaki, and Ryusei Inamori, “Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma”, Korean J Radiol, 1303-1305, Dec 24 2023.
国際会議論文
Kengo Takahashi, Yuwen Zeng, Kei Ichiji, et al., “Potential of Masked Vision Transformer for Predicting Macrovascular Invasion on Liver CT Images: Graph-Based Association Analysis of Attention Heads”, 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), Apr 2026.
Kengo Takahashi, Yuwen Zeng, Kei Ichiji, et al., “Vision Transformer for Breast Cancer Diagnosis: Layer-Wise Analyses Beyond Attention Mechanism”, 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), Apr 2026.
Kengo Takahashi, Ryusei Inamori, Kei Ichiji, et al., “Adaptive region-oriented masked vision retentive network for predicting macrovascular invasion in hepatocellular carcinoma”, Medical Imaging 2025: Computer-Aided Diagnosis, Apr 2025.
Ryusei Inamori, A DEEP LEARNING APPROACH FOR PERSONAL IDENTIFICATION FROM POSTMORTEM CT AND ANTEMORTEM CHEST X-RAYS, 15th Congress of the International Society of Forensic Radiology and Imaging, 2026/05/27（Oral）
稲森瑠星, Development of a deep learning-based model for personal identification using postmortem CT and ante-mortem chest x-rays, 第82回日本放射線技術学会総会学術大会, 2026/04/19（口頭）
