CRCFound: A Colorectal Cancer CT Image Foundation Model Based on Self‐Supervised Learning
Accurate risk stratification is crucial for determining the optimal treatment plan for patients with colorectal cancer (CRC). However, existing deep learning models perform poorly in the preoperative diagnosis of CRC and exhibit limited generalizability, primarily due to insufficient annotated data. To address these issues, CRCFound, a self-supervised learning-based CT image foundation model for CRC is proposed. After pretraining on 5137 unlabeled CRC CT images, CRCFound can learn universal feature representations and provide efficient and reliable adaptability for various clinical applications. Comprehensive benchmark tests are conducted on six different diagnostic tasks and two prognosis tasks to validate the performance of the pretrained model. Experimental results demonstrate that CRCFound can easily transfer to most CRC tasks and exhibit outstanding performance and generalization ability. Overall, CRCFound can solve the problem of insufficient annotated data and perform well in a wide range of downstream tasks of CRC, making it a promising solution for accurate diagnosis and personalized treatment of CRC patients.
Authors
- Yixue Li (ORCID: https://orcid.org/0000-0002-1198-7176)
- Chenghang Li (ORCID: https://orcid.org/0000-0003-3973-4362)
- Zhenfeng Zhuang
- Du Cai (ORCID: https://orcid.org/0000-0002-8894-7973)
- Chuling Hu (ORCID: https://orcid.org/0000-0002-0922-5551)
- Feng Gao (ORCID: https://orcid.org/0000-0002-0500-5527)
- Feng‐ao Wang
- Jing Yang (ORCID: https://orcid.org/0000-0003-2157-3003)
- Baowen Gai (ORCID: https://orcid.org/0009-0005-5934-4267)
- Yiping Chen (ORCID: https://orcid.org/0000-0002-4973-0296)
- Junwei Liu (ORCID: https://orcid.org/0000-0002-2982-2628)
- Yibin Zhao (ORCID: https://orcid.org/0000-0001-5031-7725)
- Liansheng Wang (ORCID: https://orcid.org/0000-0002-2096-454X)
- Xiaojian Wu (ORCID: https://orcid.org/0000-0001-5610-2530)
Institutions
- Sun Yat-sen University (CN)
- Shanghai Jiao Tong University (CN)
- Xiamen University (CN)
- Fudan University (CN)
- Guangzhou Experimental Station (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Shanghai Institute of Nutrition and Health (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality (CN)
- Ningbo Medical Center Lihuili Hospital (CN)
- Shanghai Institute of Pharmaceutical Industry (CN)
- University of Chinese Academy of Sciences (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
- Xiamen University of Technology (CN)
- University of Hong Kong (HK)
- South China University of Technology (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2025-08-12
- DOI
- https://doi.org/10.1002/advs.202407339
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China
- National Science and Technology Major Project
- Basic and Applied Basic Research Foundation of Guangdong Province