  Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data
science competition standpoint, have limited utility in clinical use because of
their narrow focus on diagnosing one specific disease. In real-world clinical
use, multiple diseases need to be considered since they can co-exist in the
same patient. In this work, we demonstrate how federated learning (FL) can be
used to make these toy CXR datasets from Kaggle clinically useful.
Specifically, we train a single FL classification model (`global`) using two
separate CXR datasets -- one annotated for presence of pneumonia and the other
for presence of pneumothorax (two common and life-threatening conditions) --
capable of diagnosing both. We compare the performance of the global FL model
with models trained separately on both datasets (`baseline`) for two different
model architectures. On a standard, naive 3-layer CNN architecture, the global
FL model achieved AUROC of 0.84 and 0.81 for pneumonia and pneumothorax,
respectively, compared to 0.85 and 0.82, respectively, for both baseline models
(p>0.05). Similarly, on a pretrained DenseNet121 architecture, the global FL
model achieved AUROC of 0.88 and 0.91 for pneumonia and pneumothorax,
respectively, compared to 0.89 and 0.91, respectively, for both baseline models
(p>0.05). Our results suggest that FL can be used to create global `meta`
models to make toy datasets from Kaggle clinically useful, a step forward
towards bridging the gap from bench to bedside.
