  We demonstrate that Domain Invariant Feature Learning (DIFL) can improve the
out-of-domain generalizability of a deep learning Tuberculosis screening
algorithm. It is well known that state of the art deep learning algorithms
often have difficulty generalizing to unseen data distributions due to "domain
shift". In the context of medical imaging, this could lead to unintended biases
such as the inability to generalize from one patient population to another. We
analyze the performance of a ResNet-50 classifier for the purposes of
Tuberculosis screening using the four most popular public datasets with
geographically diverse sources of imagery. We show that without domain
adaptation, ResNet-50 has difficulty in generalizing between imaging
distributions from a number of public Tuberculosis screening datasets with
imagery from geographically distributed regions. However, with the
incorporation of DIFL, the out-of-domain performance is greatly enhanced.
Analysis criteria includes a comparison of accuracy, sensitivity, specificity
and AUC over both the baseline, as well as the DIFL enhanced algorithms. We
conclude that DIFL improves generalizability of Tuberculosis screening while
maintaining acceptable accuracy over the source domain imagery when applied
across a variety of public datasets.

Image and Video Processing;Computer Vision and Pattern Recognition