  The use of ImageNet pre-trained networks is becoming widespread in the
medical imaging community. It enables training on small datasets, commonly
available in medical imaging tasks. The recent emergence of a large Chest X-ray
dataset opened the possibility for learning features that are specific to the
X-ray analysis task. In this work, we demonstrate that the features learned
allow for better classification results for the problem of Tuberculosis
detection and enable generalization to an unseen dataset. To accomplish the
task of feature learning, we train a DenseNet-121 CNN on 112K images from the
ChestXray14 dataset which includes labels of 14 common thoracic pathologies. In
addition to the pathology labels, we incorporate metadata which is available in
the dataset: Patient Positioning, Gender and Patient Age. We term this
architecture MetaChexNet. As a by-product of the feature learning, we
demonstrate state of the art performance on the task of patient Age \& Gender
estimation using CNN's. Finally, we show the features learned using ChestXray14
allow for better transfer learning on small-scale datasets for Tuberculosis.
