论文标题
使用图像和空间变压器网络对多站点数据的图像级统一
Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks
论文作者
论文摘要
我们研究了图像和空间变压器网络（ISTN）来解决多站点医学成像数据中的域移位。通常，域的适应性（DA）几乎不考虑解释性域间变换的性能，并且通常是在潜在空间中的特征级别进行的。我们在图像级上采用DA的ISTN，将转换限制为可解释的外观和形状变化。作为概念验证，我们证明可以在模拟的2D数据的分类问题上对ISTN进行对流训练。为了实现Real-DATA验证，我们从CAM-CAN和UK Biobank研究中构建了两个3D Brain MRI数据集，以研究由于收购和人口差异而引起的域转移。我们表明，在ISTN输出训练的年龄回归和性别分类模型在对数据的数据进行培训并在另一个站点上进行测试时，会改善概括。
We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explainability of the inter-domain transformation and is often conducted at the feature-level in the latent space. We employ ISTNs for DA at the image-level which constrains transformations to explainable appearance and shape changes. As proof-of-concept we demonstrate that ISTNs can be trained adversarially on a classification problem with simulated 2D data. For real-data validation, we construct two 3D brain MRI datasets from the Cam-CAN and UK Biobank studies to investigate domain shift due to acquisition and population differences. We show that age regression and sex classification models trained on ISTN output improve generalization when training on data from one and testing on the other site.
