Attention‐enhanced multiscale feature fusion network for pancreas and tumor segmentation
We propose an effective deep learning network for pancreas and tumor segmentation from abdominal CT scans. The proposed modules can better leverage global dependencies and semantic information and achieve significantly higher accuracy than the previous SOTA methods.
Authors
- Jingsong Li (ORCID: https://orcid.org/0000-0002-1064-637X)
- Xueli Bai (ORCID: https://orcid.org/0000-0002-2934-0880)
- Tianshu Zhou (ORCID: https://orcid.org/0000-0002-5858-6353)
- Yu Tian (ORCID: https://orcid.org/0000-0002-6791-8217)
- Yan Zhu (ORCID: https://orcid.org/0000-0002-5804-8926)
- Xiang Li (ORCID: https://orcid.org/0000-0002-1047-3229)
- Peijun Hu (ORCID: https://orcid.org/0000-0002-5593-1553)
- Tingbo Liang (ORCID: https://orcid.org/0000-0003-0143-3353)
- Kaiqi Dong (ORCID: https://orcid.org/0000-0003-3896-1408)
Institutions
- Zhejiang Lab (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-09-22
- DOI
- https://doi.org/10.1002/mp.17385
- Citations
- 13
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.52
Funders
- National Natural Science Foundation of China