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.
翻译:主要由于数据隐私以及获取协议或硬件的差异,大型图像数据集有限,这是开发医学领域准确和通用机器学习方法中的一个重大问题。对于磁共振(MR)图像来说尤其如此,因为不同的磁共振(MR)扫描仪引入了一种偏差,限制了机器学习模型的性能。我们提出了一个新颖的方法,通过对潜质空间引入具体的额外限制,学会忽略MR图像中与扫描仪有关的特征。我们侧重于现实世界分类设想,其中只有小数据集提供所有类别的图像。我们的方法\textit{Learn to ignit(L2I)}在多站MR数据集中,在多个感官患者和健康控制者之间的分类任务中,优于最先进的域适应方法。