Asymmetry-Aware Pretraining: A Path to Universal Brain MRI Analysis Model

Motivation: Many brain disorders present with asymmetrical features, which can be utilized to advance the classification of disease through deep learning. Goal(s): We aim to investigate how asymmetry awareness can improve model performance to identify brain disorders, subsequently facilitating better early diagnosis and intervention. Approach: Our approach involved initial pretraining with a focus on asymmetry awareness using conditional contrastive learning. Subsequently, finetuning was performed for various downstream tasks with limited training data. Results: Our approach is proven superior in identifying brain disorders compared with baseline methods that either directly trained for specific diseases or with pretrained model without considering symmetrical features of input images. Impact: Our research pioneers the integration of asymmetry awareness in pretraining models for the detection of brain disorders. The superior performance fully illustrated the feasibility and potential of leveraging the asymmetry nature of brain disease for broad deep learning tasks.