Multimodal deep learning for predicting WHO/ISUP grading in renal tumors on CT using a self-attention-based model: variable Vision Transformer (vViT)
- DOI
- 10.1016/j.ejro.2026.100796
- Published
- 2026-12
- Container
- European Journal of Radiology Open
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ejro.2026.100796,
title = {Multimodal deep learning for predicting WHO/ISUP grading in renal tumors on CT using a self-attention-based model: variable Vision Transformer (vViT)},
author = {Takuma Usuzaki and Eriya Matsuno and Takashi Shizukuishi and Ryusei Inamori and Yuwen Zeng and Xiaoyong Zhang and Sota Oguro and Noriyasu Homma and Kei Takase},
year = {2026},
journal = {European Journal of Radiology Open},
doi = {10.1016/j.ejro.2026.100796},
url = {https://doi.org/10.1016/j.ejro.2026.100796}
}RIS
TY - JOUR TI - Multimodal deep learning for predicting WHO/ISUP grading in renal tumors on CT using a self-attention-based model: variable Vision Transformer (vViT) AU - Takuma Usuzaki AU - Eriya Matsuno AU - Takashi Shizukuishi AU - Ryusei Inamori AU - Yuwen Zeng AU - Xiaoyong Zhang AU - Sota Oguro AU - Noriyasu Homma AU - Kei Takase PY - 2026 JO - European Journal of Radiology Open DO - 10.1016/j.ejro.2026.100796 UR - https://doi.org/10.1016/j.ejro.2026.100796 ER -
APA
Usuzaki, T., Matsuno, E., Shizukuishi, T., Inamori, R., Zeng, Y., Zhang, X., Oguro, S., Homma, N., & Takase, K. (2026). 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. https://doi.org/10.1016/j.ejro.2026.100796
Source records
- crossref · retrieved 2026-09-26T03:20:25.467Z