Development of deep learning model for diagnosing muscle-invasive bladder cancer on MRI with vision transformer
The DL model effectively diagnosed MIBC. The ViT-based model outperformed CNN-based models, highlighting its utility in medical image analysis.
Authors
- Satoru Takahashi (ORCID: https://orcid.org/0000-0002-8139-8158)
- Takashi Kobayashi (ORCID: https://orcid.org/0000-0003-1069-2816)
- Yuki Himoto (ORCID: https://orcid.org/0000-0001-8508-8221)
- Aki Kido (ORCID: https://orcid.org/0000-0001-5131-2870)
- Yusaku Moribata (ORCID: https://orcid.org/0000-0002-9055-3922)
- Mizuho Nishio (ORCID: https://orcid.org/0000-0001-5870-0868)
- Yasuhisa Kurata (ORCID: https://orcid.org/0000-0002-2002-6836)
- Kimihiko Masui (ORCID: https://orcid.org/0000-0002-7841-1104)
- Jiro Kusakabe (ORCID: https://orcid.org/0000-0002-8552-3147)
- Hiroyuki Onishi (ORCID: https://orcid.org/0000-0002-9391-8253)
- Keisuke Hidaka
- Yuji Nakamoto
- Naoko Nishio
- Ryota Okura
- Takehiko Segawa
- Satoshi Otani
- Akihiko Furuta
- Marina Shimizu
Institutions
- Cleveland Clinic (US)
- Kyoto University (JP)
- Kyoto City Hospital (JP)
- Kyoto University Hospital (JP)
- Osaka Red Cross Hospital (JP)
- Shiga Medical Center (JP)
- Takatsuki General Hospital (JP)
Publication Details
- Journal
- Heliyon
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e36144
- Citations
- 13
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 7.45
Funders
- Japan Society for the Promotion of Science