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Background & Motivation: Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from where the performance deep neural networks for thorax disease recognition is severely limited by the availability of only 4143 frontal view images (Openi is the previous largest publicly available chest X-ray dataset to date).In this database, we provide an enhanced version (with 6 more disease categories and more images as well) of the dataset used in the recent work which is approximately 27 times of the number of frontal chest x-ray images . Our dataset is extracted from the clinical PACS database at National Institutes of Health Clinical Center and consists of ~60% of all frontal chest x-rays in the hospital. Therefore we expect this dataset is significantly more representative to the real patient population distributions and realistic clinical diagnosis challenges, than any previous chest x-ray datasets. Of course, the size of our dataset, in terms of the total numbers of images and thorax disease frequencies, would better facilitate deep neural network training . Refer to on the details of how the dataset is extracted and image labels are mined through natural language processing (NLP).