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: this https URL

该研究采用大规模方法量化 X 射线诊断预测任务在多个不同数据集上广泛适用的程度，并发现预测的不一致性不是由图像转移引起的，而是由标签转移引起的。通过正则化网络来测试概念相似性，观察多个数据集中任务之间的差异。

自动 X 光预测中跨域普适性的局限性研究

On the limits of cross-domain generalization in automated X-ray  prediction

Applying machine learning in the health care domain has shown promising
results in recent years. Interpretable outputs from learning algorithms are
desirable for decision making by health care personnel. In this work, we
explore the possibility of utilizing causal relationships to refine diagnostic
prediction. We focus on the task of diagnostic prediction using discomfort
drawings, and explore two ways to employ causal identification to improve the
diagnostic results. Firstly, we use causal identification to infer the causal
relationships among diagnostic labels which, by itself, provides interpretable
results to aid the decision making and training of health care personnel.
Secondly, we suggest a post-processing approach where the inferred causal
relationships are used to refine the prediction accuracy of a multi-view
probabilistic model. Experimental results show firstly that causal
identification is capable of detecting the causal relationships among
diagnostic labels correctly, and secondly that there is potential for improving
pain diagnostics prediction accuracy using the causal relationships.

本篇论文研究了利用因果关系来改进医疗领域中的疾病诊断预测模型，通过因果关系的推断提高医生的决策能力和模型的预测准确性。