  Recent advances in MRI have led to the creation of large datasets. With the
increase in data volume, it has become difficult to locate previous scans of
the same patient within these datasets (a process known as re-identification).
To address this issue, we propose an AI-powered medical imaging retrieval
framework called DeepBrainPrint, which is designed to retrieve brain MRI scans
of the same patient. Our framework is a semi-self-supervised contrastive deep
learning approach with three main innovations. First, we use a combination of
self-supervised and supervised paradigms to create an effective brain
fingerprint from MRI scans that can be used for real-time image retrieval.
Second, we use a special weighting function to guide the training and improve
model convergence. Third, we introduce new imaging transformations to improve
retrieval robustness in the presence of intensity variations (i.e. different
scan contrasts), and to account for age and disease progression in patients. We
tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the
Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset
designed to evaluate retrieval performance with different image modalities. Our
results show that DeepBrainPrint outperforms previous methods, including simple
similarity metrics and more advanced contrastive deep learning frameworks.
