Lesion segmentation method for multiple types of liver cancer based on balanced dice loss
The differences in CT images between different types of liver cancer necessitate deep learning models to learn distinct features. Our method addresses this challenge, enabling balanced and accurate segmentation performance across multiple types of liver cancer.
Authors
- Meiyi Yang (ORCID: https://orcid.org/0000-0003-4548-4945)
- Bin Song (ORCID: https://orcid.org/0000-0002-8096-3370)
- Lifeng Xu (ORCID: https://orcid.org/0000-0003-4957-7559)
- Xue‐Lei Li
- Yu Gong
- Haoyang Jia
- Jiajun Zhou (ORCID: https://orcid.org/0000-0002-5062-4183)
- Tongtong Li (ORCID: https://orcid.org/0000-0001-7506-5368)
- Ming Liu (ORCID: https://orcid.org/0000-0003-3997-6185)
- Jun Xie
- Yi Wei (ORCID: https://orcid.org/0000-0003-3993-9747)
Institutions
- University of Electronic Science and Technology of China (CN)
- Sichuan University (CN)
- Wenzhou Medical University (CN)
- West China Hospital of Sichuan University (CN)
- Quzhou University (CN)
- Sanya Central Hospital (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1002/mp.17624
- Citations
- 3
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- West China Hospital, Sichuan University