Enhancing CT image segmentation accuracy through ensemble loss function optimization
Our findings support the efficacy of using ensemble models with optimized weights to improve segmentation accuracy, highlighting the potential for broader applications in automated medical image analysis.
Authors
- Kundan Thind (ORCID: https://orcid.org/0000-0002-8261-6655)
- Dongxiao Zhu (ORCID: https://orcid.org/0000-0002-0225-7817)
- Chengyin Li (ORCID: https://orcid.org/0000-0003-2450-9760)
- Qiang Yao (ORCID: https://orcid.org/0000-0003-2190-6410)
- Rafi Ibn Sultan
- Indrin J. Chetty (ORCID: https://orcid.org/0000-0002-7979-7053)
- Hassan Bagher‐Ebadian (ORCID: https://orcid.org/0000-0002-4835-9237)
Institutions
- Cedars-Sinai Medical Center (US)
- Henry Ford Health System (US)
- Oakland University (US)
- Wayne State University (US)
- Michigan State University (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1002/mp.17848
- Citations
- 2
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.55