For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning. Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framework to adapt it to learn across multiple sites. We tested our meta-learning model for classifying ASD versus typically developing controls in 2,201 T1-weighted (T1-w) MRI scans collected from 38 imaging sites as part of Autism Brain Imaging Data Exchange (ABIDE) [age: 5.2-64.0 years]. The method was trained to find a good initialization state for our model that can quickly adapt to data from new unseen sites by fine-tuning on the limited data that is available. The proposed method achieved an ROC-AUC=0.857 on 370 scans from 7 unseen sites in ABIDE using a few-shot setting of 2-way 20-shot i.e., 20 training samples per site. Our results outperformed a transfer learning baseline by generalizing across a wider range of sites as well as other related prior work. We also tested our model in a zero-shot setting on an independent test site without any additional fine-tuning. Our experiments show the promise of the proposed site-agnostic meta-learning framework for challenging neuroimaging tasks involving multi-site heterogeneity with limited availability of training data.

翻译：在医学影像的机器学习应用中，训练数据的可用性往往有限，这阻碍了针对自闭症谱系障碍等细微病症的放射学分类器的设计。迁移学习是应对训练数据不足问题的一种方法。本文探讨了在拥有来自多个站点的先前数据背景下，针对极低数据场景的元学习应用——我们将其称为站点不可知元学习方法。受元学习在多任务间优化模型有效性的启发，我们提出了一个框架，使其能够跨多个站点进行学习。我们测试了该元学习模型对自闭症谱系障碍（ASD）与典型发育对照进行分类的效果，数据来自自闭症脑成像数据交换（ABIDE）项目中38个成像站点收集的2201例T1加权MRI扫描（年龄：5.2-64.0岁）。该方法通过训练找到模型的良好初始化状态，使其能够通过微调有限可用数据，快速适应来自未见新站点的数据。在ABIDE中7个未见站点的370例扫描上，采用2-way 20-shot（即每个站点20个训练样本）的小样本设置，所提出的方法达到了ROC-AUC=0.857。我们的结果优于基线迁移学习方法，在更广泛的站点上实现了泛化，并超越了其他相关先前工作。我们还在一处独立测试站点上以零样本设置测试了模型（无需额外微调）。实验表明，所提出的站点不可知元学习框架在涉及多站点异质性与训练数据有限挑战的神经影像任务中具有潜力。