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An adversarial approach for the robust classification of pneumonia from chest radiographs
Proceedings of the ACM Conference on Health, Inference, and Learning
While deep learning has shown promise in the domain of disease classication from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when tested on data from the same hospital, but perform signicantly worse when they are tested in dierent hospital systems. Furthermore, even within a given hospital system, deepdoi:10.1145/3368555.3384458 dblp:conf/chil/JanizekEDL20 fatcat:bovemdso7jddndlceujn6znuk4