Multimodal deep learning with MUF-net for noninvasive WHO/ISUP grading of renal cell carcinoma using CEUS and B-mode ultrasound
MUF-Net provides accurate and interpretable RCC nuclear grading, surpassing unimodal approaches, with Grad-CAM offering intuitive insights into the model's predictions.
Authors
- Lin Zhu (ORCID: https://orcid.org/0000-0001-6929-9205)
- Ji Wu
- Hao‐Lun Luo (ORCID: https://orcid.org/0000-0002-5988-0474)
- Yixin Zhu (ORCID: https://orcid.org/0000-0001-7024-1545)
- Qiongxian Long
- Yan Li (ORCID: https://orcid.org/0000-0002-4942-8863)
- Hui Luo
- Lu Pang
Institutions
- North Sichuan Medical University (CN)
- Jinan University (CN)
- Peking University Shenzhen Hospital (CN)
- Nanchong Central Hospital (CN)
- Mindray (China) (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-03-07
- DOI
- https://doi.org/10.3389/fphys.2025.1558997
- Citations
- 1
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52