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Magnetic resonance imaging~(MRI) have played a crucial role in brain diseasediagnosis, with which a range of computer-aided artificial intelligence methodshave been proposed. However, the early explorations usually focus on thelimited types of brain diseases in one study and train the model on the data ina small scale, yielding the bottleneck of generalization. Towards a moreeffective and scalable paradigm, we propose a hierarchical knowledge-enhancedpre-training framework for the universal brain MRI diagnosis, termed asUniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770imaging-report pairs from routine diagnostics. Different from previouspre-training techniques for the unitary vision or textual feature, or with thebrute-force alignment between vision and language information, we leverage theunique characteristic of report information in different granularity to build ahierarchical alignment mechanism, which strengthens the efficiency in featurelearning. Our UniBrain is validated on three real world datasets with severeclass imbalance and the public BraTS2019 dataset. It not only consistentlyoutperforms all state-of-the-art diagnostic methods by a large margin andprovides a superior grounding performance but also shows comparable performancecompared to expert radiologists on certain disease types.

UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training