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Computer-aided techniques may lead to more accurate and more acces-siblediagnosis of thorax diseases on chest radiography. Despite the success of deeplearning-based solutions, this task remains a major challenge in smarthealthcare, since it is intrinsically a weakly supervised learning problem. Inthis paper, we incorporate the attention mechanism into a deep convolutionalneural network, and thus propose the ChestNet model to address effectivediagnosis of thorax diseases on chest radiography. This model consists of twobranches: a classification branch serves as a uniform featureextraction-classification network to free users from troublesome handcraftedfeature extraction, and an attention branch exploits the correlation betweenclass labels and the locations of patholog-ical abnormalities and allows themodel to concentrate adaptively on the patholog-ically abnormal regions. Weevaluated our model against three state-of-the-art deep learning models on theChest X-ray 14 dataset using the official patient-wise split. The resultsindicate that our model outperforms other methods, which use no extra trainingdata, in diagnosing 14 thorax diseases on chest radiography.

ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography