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We investigate the generalizability of deep convolutional neural network(CNN) on the task of disease classification from chest x-rays collected overmultiple sites. We systematically train the model using datasets from threeindependent sites with different patient populations: National Institute ofHealth (NIH), Stanford University Medical Centre (CheXpert), and ShifaInternational Hospital (SIH). We formulate a sequential training approach anddemonstrate that the model produces generalized prediction performance usingheld out test sets from the three sites. Our model generalizes better whentrained on multiple datasets, with the CheXpert-Shifa-NET model performingsignificantly better (p-values < 0.05) than the models trained on individualdatasets for 3 out of the 4 distinct disease classes. The code for training themodel will be made available open source at: www.github.com/link-to-code at thetime of publication.

A generalized deep learning model for multi-disease Chest X-Ray diagnostics