Multi-encoder U-Net benchmarking for LiTS17 Liver–Tumor segmentation: accuracy–efficiency trade-offs across training durations
Overall, the study provides a reproducible protocol and practical guidance for selecting segmentation models that balance accuracy, robustness, and deployment cost.
Authors
- Jingyi Huang (ORCID: https://orcid.org/0000-0002-1209-9699)
- Taisheng Zeng (ORCID: https://orcid.org/0000-0001-7806-0351)
- Kavimbi Chipusu
- Yuehua Chen (ORCID: https://orcid.org/0009-0006-1343-7449)
- Yuguang Ye (ORCID: https://orcid.org/0000-0002-1059-5664)
- Bijiao Ding
- Yifeng Huang
- Jingwei Guo
Institutions
- Huaqiao University (CN)
- Fujian Medical University (CN)
- Quanzhou Normal University (CN)
Publication Details
- Journal
- Biomedizinische Technik/Biomedical Engineering
- Published
- 2026-02-16
- DOI
- https://doi.org/10.1515/bmt-2026-0021
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00