Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis
This addresses the issue of limited and biased medical image datasets for researchers and clinicians, though it is incremental as it builds on existing domain adaptation methods.
The paper tackled the problem of scanner bias in multi-site MRI analysis by developing a method that learns to ignore scanner-related features, achieving state-of-the-art performance in classifying multiple sclerosis patients versus healthy controls.
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.