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 \textit{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.
@article{arxiv.2110.06803,
title = {Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis},
author = {Julia Wolleb and Robin Sandkühler and Florentin Bieder and Muhamed Barakovic and Nouchine Hadjikhani and Athina Papadopoulou and Özgür Yaldizli and Jens Kuhle and Cristina Granziera and Philippe C. Cattin},
journal= {arXiv preprint arXiv:2110.06803},
year = {2022}
}