DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification

Lemuel Puglisi, Frederik Barkhof, Daniel C. Alexander, Geoffrey JM Parker, Arman Eshaghi, Daniele Ravi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
Subtitle of host publicationMedical Imaging with Deep Learning
EditorsIpek Oguz, Jack Noble, Xiaoxiao Li, Martin Styner, Christian Baumgartner, Mirabela Rusu, Tobias Heinmann, Despina Kontos, Bennett Landman, Benoit Dawant
Place of PublicationUSA
Number of pages14
Publication statusPublished - 12 Jul 2023

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)2640-3498


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