A deep learning model based on self-supervised learning for identifying subtypes of proliferative hepatocellular carcinoma from dynamic contrast-enhanced MRI
The proposed model enables noninvasive identification of HCC with high proliferation and aggressive behavior. SSL improves lesion differentiation by reducing redundancy and enhancing feature diversity. Dynamic feature extraction captures vascular infiltration, aiding preoperative metastasis risk assessment.
Authors
- Yiling Li (ORCID: https://orcid.org/0000-0003-3209-8105)
- Yuan Miao (ORCID: https://orcid.org/0000-0001-7869-8099)
- Xuedan Li (ORCID: https://orcid.org/0009-0002-9682-0136)
- Ronghui Ju (ORCID: https://orcid.org/0009-0008-2284-6812)
- Xiaoyu Cui (ORCID: https://orcid.org/0000-0001-7180-1583)
- Shuairan Zhang (ORCID: https://orcid.org/0009-0009-0498-3079)
- Hui Qu (ORCID: https://orcid.org/0000-0002-4260-3809)
- Yuxi Han (ORCID: https://orcid.org/0000-0003-4020-6268)
Institutions
- Liaoning Provincial People's Hospital (CN)
- First Hospital of China Medical University (CN)
- China Medical University (CN)
- Northeastern University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1186/s13244-025-01968-w
- Citations
- 8
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 10.88
Funders
- Natural Science Foundation of Liaoning Province
- Department of Education of Liaoning Province
- Scientific Research Fund of Liaoning Provincial Education Department
- National Science and Technology Major Project