DynTransNet: Dynamic Transformer Network with multi-scale attention for liver cancer segmentation
The proposed method offers a robust, efficient tool for liver tumor segmentation and holds strong potential to streamline diagnostic workflows and improve automated medical image analysis in clinical practice.
Authors
- Yu Chen (ORCID: https://orcid.org/0000-0002-1952-0466)
- A. S. M. Sharifuzzaman Sagar (ORCID: https://orcid.org/0000-0003-4891-0233)
- Yongyi Zeng (ORCID: https://orcid.org/0000-0001-8823-698X)
- Siming Zheng (ORCID: https://orcid.org/0000-0002-4126-2547)
- Ying Shi (ORCID: https://orcid.org/0000-0002-9670-6029)
- Zehao Yu
Institutions
- University College of the Cayman Islands (KY)
- Fujian Medical University (CN)
- Ningbo University of Technology (CN)
- Sejong University (KR)
- Mengchao Hepatobiliary Hospital (CN)
- Robotics Research (United States) (US)
- First Affiliated Hospital of Fujian Medical University (CN)
- Truman Bodden Law School (KY)
- Ningbo First Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-06-19
- DOI
- https://doi.org/10.3389/fonc.2025.1569083
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Medical Science and Technology Project of Zhejiang Province
- Science and Technology Program of Zhejiang Province