CRCFound: A Colorectal Cancer CT Image Foundation Model Based on Self-Supervised Learning.
Authors
Affiliations (16)
Affiliations (16)
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.
- Department of General Surgery (Department of Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China.
- Guangzhou National Laboratory, Guangzhou, 510655, China.
- Department of Computer Science at the School of Informatics, Xiamen University, Xiamen, 361005, China.
- Department of Colorectal Surgery, Ningbo Medical Center Lihuili Hospital (Affiliated Lihuili Hospital of Ningbo University), Ningbo, 315000, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
- Artificial Intelligence Thrust, The Hong Kong University of Science and Technology, Guangzhou, 510655, China.
- School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, 510655, China.
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200030, China.
- GZMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou, 511436, China.
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, 200433, China.
- Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200032, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200433, China.
Abstract
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.