Abstract
Combining large multi-center datasets can enhance statistical power, particularly in the field of neurology, where data can be scarce. However, applying a deep learning model trained on existing neuroimaging data often leads to inconsistent results when tested on new data due to domain shift caused by differences between the training (source domain) and testing (target domain) data. Existing literature offers several solutions based on domain adaptation (DA) techniques, which ignore complex practical scenarios where heterogeneity may exist in the source or target domain. This study proposes a new perspective in solving the domain shift issue for MRI data by identifying and addressing the dominant factor causing heterogeneity in the dataset. We design an unsupervised DA method leveraging the maximum mean discrepancy and correlation alignment loss in order to align domain-invariant features. Instead of regarding the entire dataset as a source or target domain, the dataset is processed based on the dominant factor of data variations, which is the scanner manufacturer. Afterwards, the target domain’s feature space is aligned pairwise with respect to each source domain’s feature map. Experimental results demonstrate significant performance gain for multiple inter- and intra-study neurodegenerative disease classification tasks. Source code available at (https://github.com/rkushol/DAMS).
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Data use declaration and acknowledgment
ADNI1, ADNI2, and AIBL neuroimaging data were collected from the ADNI portal (adni.loni.usc.edu) through a standard application process. The MIRIAD dataset was obtained from (http://miriad.drc.ion.ucl.ac.uk). This study has been supported by the Canadian Institutes of Health Research (CIHR), ALS Society of Canada, Brain Canada Foundation, Natural Sciences and Engineering Research Council of Canada (NSERC), and Prime Minister Fellowship Bangladesh.
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Kushol, R., Frayne, R., Graham, S.J., Wilman, A.H., Kalra, S., Yang, YH. (2024). Domain Adaptation of MRI Scanners as an Alternative to MRI Harmonization. In: Koch, L., et al. Domain Adaptation and Representation Transfer. DART 2023. Lecture Notes in Computer Science, vol 14293. Springer, Cham. https://doi.org/10.1007/978-3-031-45857-6_1
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