Abstract
Deep learning has shown record-shattering performance in multiple medical tasks. However, data quantity and quality are crucial requirements. As a matter of fact, data is one of the most challenging issues while deploying deep learning models for different tasks. One of the main challenges is the institutions’ privacy protocols, in particular in the medical field. Indeed, the metadata is usually excluded from the database provided. Many invisible features in images can help tracing anonymized data. We propose to use deep learning to exclude these traces. This article focuses on Magnetic resonance imaging (MRI) and one of the most important features, the equipment used for acquisition. First, we aim to produce an algorithm able to perform well distinguishing multiple MRI equipment from different brands. To this end, we employ a convolution neural network architecture to work on this medical image classification task. The second part of this paper is dedicated to reconstructing the input MRI using a simple auto-encoder. The latter step is to use the auto-encoder in order to mislead the classifier classifying the MRI equipment.
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Acknowledgements
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.;Cogstate;Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.;NeuroRx Research; Neurotrack Technologies;Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics.The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
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This study was funded by the University of Poitiers doctoral scholarship.
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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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Fezai, L., Urruty, T., Bourdon, P. et al. Deep anonymization of medical imaging. Multimed Tools Appl 82, 9533–9547 (2023). https://doi.org/10.1007/s11042-022-13686-2
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DOI: https://doi.org/10.1007/s11042-022-13686-2