A lung CT vision foundation model facilitating disease diagnosis and medical imaging.
Authors
Affiliations (16)
Affiliations (16)
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
- School of Information Science and Technology, Fudan University, Shanghai, China.
- Department of Automation, Tsinghua University, Beijing, China.
- Department of Thoracic Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Tsinghua Shenzhen International Graduate School, Tsinghua University, ShenZhen, China.
- School of Software, Tsinghua University, Beijing, China.
- Hangzhou Zhuoxi Institute of Brain and Intelligence, Hangzhou, China.
- Department of Thoracic Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. [email protected].
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China. [email protected].
- School of Software, Tsinghua University, Beijing, China. [email protected].
- Tsinghua Medicine, Tsinghua University, Beijing, China. [email protected].
- Beijing Visual Science and Translational Eye Institute, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China. [email protected].
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China. [email protected].
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China. [email protected].
- School of Information Science and Technology, Fudan University, Shanghai, China. [email protected].
- Department of Automation, Tsinghua University, Beijing, China. [email protected].
Abstract
The concomitant development and evolution of lung computed tomography (CT) and artificial intelligence (AI) have made non-invasive lung imaging a key component of clinical care of patients. However, the scarcity of labeled CT data and the limited generative capacity of existing models have constrained their clinical utility. Here, we present LCTfound, a large-scale vision foundation model designed to overcome these limitations. Trained on a multi-center dataset comprising 105,184 CT scans, LCTfound leverages diffusion-based pretraining and joint encoding of imaging and clinical information to support 8 tasks, including CT enhancement, virtual computed tomography angiography (CTA), sparse-view reconstruction, lesion segmentation, diagnosis, prognosis, cancer pathological response prediction, and three-dimensional surgical navigation. In comprehensive multicenter evaluations, LCTfound consistently outperforms leading baseline models, delivering a unified, broadly deployable solution that both augments clinical decision-making and elevates CT image quality across diverse practice settings. LCTfound establishes a scalable foundation for next-generation clinical imaging intelligence, uniting large AI model with precision healthcare.