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

Puglisi, Lemuel; Barkhof, Frederik; Alexander, Daniel C; Parker, Geoffrey JM; Eshaghi, Arman; Ravì, Daniele; (2023) DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification. arXiv.org: Ithaca (NY), USA. Green open access

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Abstract

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.

Type: Working / discussion paper
Title: DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.48550/arXiv.2302.13057
Language: English
Additional information: © The Authors 2023. Original content in this paper is licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Licence (https://creativecommons.org/licenses/by-nc-sa/4.0/).
Keywords: Brain MRI Fingerprint, Re-Identification, Deep metric Learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10182290
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