The limited availability of large image datasets, mainly due to data privacy and differences in acquisition protocols or hardware, is a significant issue in the development of accurate and generalizable machine learning methods in medicine. This is especially the case for Magnetic Resonance (MR) images, where different MR scanners introduce a bias that limits the performance of a machine learning model. We present a novel method that learns to ignore the scanner-related features present in MR images, by introducing specific additional constraints on the latent space. We focus on a real-world classification scenario, where only a small dataset provides images of all classes. Our method Learn to Ignore (L2I) outperforms state-of-the-art domain adaptation methods on a multi-site MR dataset for a classification task between multiple sclerosis patients and healthy controls.
CITATION STYLE
Wolleb, J., Sandkühler, R., Bieder, F., Barakovic, M., Hadjikhani, N., Papadopoulou, A., … Cattin, P. C. (2022). Learn to Ignore: Domain Adaptation for Multi-site MRI Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13437 LNCS, pp. 725–735). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16449-1_69
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