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The use of ImageNet pre-trained networks is becoming widespread in themedical imaging community. It enables training on small datasets, commonlyavailable in medical imaging tasks. The recent emergence of a large Chest X-raydataset opened the possibility for learning features that are specific to theX-ray analysis task. In this work, we demonstrate that the features learnedallow for better classification results for the problem of Tuberculosisdetection and enable generalization to an unseen dataset. To accomplish thetask of feature learning, we train a DenseNet-121 CNN on 112K images from theChestXray14 dataset which includes labels of 14 common thoracic pathologies. Inaddition to the pathology labels, we incorporate metadata which is available inthe dataset: Patient Positioning, Gender and Patient Age. We term thisarchitecture MetaChexNet. As a by-product of the feature learning, wedemonstrate state of the art performance on the task of patient Age \& Genderestimation using CNN's. Finally, we show the features learned using ChestXray14allow for better transfer learning on small-scale datasets for Tuberculosis.

Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset