  Magnetic resonance imaging~(MRI) have played a crucial role in brain disease
diagnosis, with which a range of computer-aided artificial intelligence methods
have been proposed. However, the early explorations usually focus on the
limited types of brain diseases in one study and train the model on the data in
a small scale, yielding the bottleneck of generalization. Towards a more
effective and scalable paradigm, we propose a hierarchical knowledge-enhanced
pre-training framework for the universal brain MRI diagnosis, termed as
UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770
imaging-report pairs from routine diagnostics. Different from previous
pre-training techniques for the unitary vision or textual feature, or with the
brute-force alignment between vision and language information, we leverage the
unique characteristic of report information in different granularity to build a
hierarchical alignment mechanism, which strengthens the efficiency in feature
learning. Our UniBrain is validated on three real world datasets with severe
class imbalance and the public BraTS2019 dataset. It not only consistently
outperforms all state-of-the-art diagnostic methods by a large margin and
provides a superior grounding performance but also shows comparable performance
compared to expert radiologists on certain disease types.
