Machine learning-based multi-class classification of bladder pathologies using fused 3D CT radiomic and 3D auto-encoder deep features
The proposed hybrid CT analysis framework achieves clinically relevant performance in the automated, multi-class classification of bladder pathologies, excelling particularly in calculi detection. The complementary roles of radiomic and deep features provide an interpretable diagnostic aid, demonstrating potential for integration into clinical workflows to support differential diagnosis.
Authors
- Zexin Huang (ORCID: https://orcid.org/0000-0003-3835-7163)
- Tianyu Wang (ORCID: https://orcid.org/0000-0001-7030-1990)
- Hanlin Liu (ORCID: https://orcid.org/0000-0001-5240-6705)
- Kai Wu (ORCID: https://orcid.org/0000-0002-1269-9918)
- Yangguang Yuan
- Huancheng Yang
- Hongwei Xiao
- Weihao Liu
Institutions
- Shenzhen University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Shenzhen Luohu People's Hospital (CN)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2026-01-09
- DOI
- https://doi.org/10.1016/j.ejro.2026.100728
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China