Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 9 code implementations](https://www.catalyzex.com/paper/arxiv:2002.02497/code)
Keywords: Chest X-ray, Radiology, Deep Learning, Generalization
TL;DR: This large scale study focuses on quantifying what X-rays diagnostic prediction tasks generalize well and which do not.
Track: full conference paper
Abstract: 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
Paper Type: methodological development
Source Latex: zip
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