MULTI-TASK LEARNING FOR CHEST X-RAY ABNORMALITY CLASSIFICATION

Systems and methods are provided for automatic classification of multiple abnormalities that are visible in chest X-ray images. The systems and methods are based on a deep learning architecture that predicts, in addition to classification scores of abnormalities, lung/heart masks, and the location of certain abnormalities. By training a multi-task network to improve all the results, the network and the resulting abnormality classification is improved. Normalization of the chest X-ray images is also used to improve the accuracy and efficiency of the multi-task network.