  Deep Learning has thrived on the emergence of biomedical big data. However,
medical datasets acquired at different institutions have inherent bias caused
by various confounding factors such as operation policies, machine protocols,
treatment preference and etc. As the result, models trained on one dataset,
regardless of volume, cannot be confidently utilized for the others. In this
study, we investigated model robustness to dataset bias using three large-scale
Chest X-ray datasets: first, we assessed the dataset bias using vanilla
training baseline; second, we proposed a novel multi-source domain
generalization model by (a) designing a new bias-regularized loss function; and
(b) synthesizing new data for domain augmentation. We showed that our model
significantly outperformed the baseline and other approaches on data from
unseen domain in terms of accuracy and various bias measures, without
retraining or finetuning. Our method is generally applicable to other
biomedical data, providing new algorithms for training models robust to bias
for big data analysis and applications. Demo training code is publicly
available.
