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

Understand

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.