Learning invariant representation for unsupervised domain adaptive thorax disease classification

•It designs a novel UDA framework that learns invariant features for optimizing thorax disease classification.•We propose a novel feature learning scheme that regularizes features concurrently via three types of invariance constraints.•We develop an end-to-end trainable deep network that significantly improves the thorax disease classification performance.