论文标题
域概括：用于医学成像中域概括的几个元素元学习框架
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging
论文作者
论文摘要
深度学习模型在目标（测试）数据域进行测试时，其分布类似于一组源（火车）域。但是，当目标域和源域之间的基本统计数据显着差异时，可能会阻碍模型概括。在这项工作中，我们将基于模型不可吻合的元学习框架的域泛化方法适应生物医学成像。该方法学习了域 - 不合稳定的特征表示，以改善模型对看不见的测试分布的概括。该方法可用于任何成像任务，因为它不取决于基础模型体系结构。我们通过在三个数据集上的健康和病理病例中跨计算机断层扫描（CT）椎骨分割任务来验证方法。接下来，我们使用很少的学习学习，即使用来自看不见的域中的很少的示例来培训广义模型，以快速使该模型适应新的看不见的数据分布。我们的结果表明，该方法可以帮助跨越不同的医疗中心，图像采集方案，解剖学，不同区域的不同成像方式中的不同区域。
Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be hindered when there is significant difference in the underlying statistics between the target and source domains. In this work, we adapt a domain generalization method based on a model-agnostic meta-learning framework to biomedical imaging. The method learns a domain-agnostic feature representation to improve generalization of models to the unseen test distribution. The method can be used for any imaging task, as it does not depend on the underlying model architecture. We validate the approach through a computed tomography (CT) vertebrae segmentation task across healthy and pathological cases on three datasets. Next, we employ few-shot learning, i.e. training the generalized model using very few examples from the unseen domain, to quickly adapt the model to new unseen data distribution. Our results suggest that the method could help generalize models across different medical centers, image acquisition protocols, anatomies, different regions in a given scan, healthy and diseased populations across varied imaging modalities.
