Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis

Julia Wolleb; Robin Sandkühler; Florentin Bieder; Muhamed Barakovic; Nouchine Hadjikhani; Athina Papadopoulou; Özgür Yaldizli; Jens Kuhle; Cristina Granziera; Philippe C. Cattin

arXiv · 2110.06803

Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis

Julia Wolleb·Robin Sandkühler·Florentin Bieder·Muhamed Barakovic·Nouchine Hadjikhani·Athina Papadopoulou·Özgür Yaldizli·Jens Kuhle·Cristina Granziera·Philippe C. Cattin

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Abstract

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.

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Julia Wolleb, Robin Sandkühler, Florentin Bieder, Muhamed Barakovic, Nouchine Hadjikhani, Athina Papadopoulou, Özgür Yaldizli, Jens Kuhle, Cristina Granziera, Philippe C. Cattin. 2021-10-13. Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis. https://arxiv.org/abs/2110.06803

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