UniBrain: Universal Brain MRI diagnosis with hierarchical knowledge-enhanced pre-training.
Authors
Affiliations (8)
Affiliations (8)
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230026, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
- Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
- Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
- Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. Electronic address: [email protected].
- School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200230, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230026, China.
- School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200230, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. Electronic address: [email protected].
Abstract
Magnetic Resonance Imaging (MRI) has become a pivotal tool in diagnosing brain diseases, with a wide array of computer-aided artificial intelligence methods being proposed to enhance diagnostic accuracy. However, early studies were often limited by small-scale datasets and a narrow range of disease types, which posed challenges in model generalization. This study presents UniBrain, a hierarchical knowledge-enhanced pre-training framework designed for universal brain MRI diagnosis. UniBrain leverages a large-scale dataset comprising 24,770 imaging-report pairs from routine diagnostics for pre-training. Unlike previous approaches that either focused solely on visual representation learning or used brute-force alignment between vision and language, the framework introduces a hierarchical alignment mechanism. This mechanism extracts structured knowledge from free-text clinical reports at multiple granularities, enabling vision-language alignment at both the sequence and case levels, thereby significantly improving feature learning efficiency. A coupled vision-language perception module is further employed for text-guided multi-label classification, which facilitates zero-shot evaluation and fine-tuning of downstream tasks without modifying the model architecture. UniBrain is validated on both in-domain and out-of-domain datasets, consistently surpassing existing state-of-the-art diagnostic models and demonstrating performance on par with radiologists in specific disease categories. It shows strong generalization capabilities across diverse tasks, highlighting its potential for broad clinical application. The code is available at https://github.com/ljy19970415/UniBrain.