论文标题
在自动X射线预测中的跨域泛化限制
On the limits of cross-domain generalization in automated X-ray prediction
论文作者
论文摘要
这项大规模研究的重点是量化哪些X射线诊断预测任务在多个不同的数据集中良好地推广。我们提供了证据，表明概括不是由于图像的变化，而是标签的变化。我们研究跨域的性能，模型之间的一致性和模型表示。我们发现绩效和一致性之间的有趣差异，在这些模型中，实现良好绩效的模型在他们的预测中不同意，以及同意却达到绩效差的模型。我们还通过将网络正规化为跨多个数据集的组成任务并观察到整个任务的变化来测试概念相似性。所有代码均可在线提供，并且数据可公开可用：https：//github.com/mlmed/torchxrayvision
This large scale study focuses on quantifying what X-rays diagnostic prediction tasks generalize well across multiple different datasets. We present evidence that the issue of generalization is not due to a shift in the images but instead a shift in the labels. We study the cross-domain performance, agreement between models, and model representations. We find interesting discrepancies between performance and agreement where models which both achieve good performance disagree in their predictions as well as models which agree yet achieve poor performance. We also test for concept similarity by regularizing a network to group tasks across multiple datasets together and observe variation across the tasks. All code is made available online and data is publicly available: https://github.com/mlmed/torchxrayvision
