Fig. 1.

Without loss of generality, we illustrate our idea in 2D with chest X-rays. The great similarity of the lungs in anatomy, partially annotated in (a), across patients yields complex yet consistent and recurring anatomical patterns across X-rays in healthy (a, b, and c) or diseased (d and e), which we refer to as anatomical visual words. Our proposed TransVW (transferable visual words) aims to learn generalizable image representation from the anatomical visual words without expert annotations, and transfer the learned deep models to create powerful application-specific target models. TransVW is general and applicable across organs, diseases, and modalities in both 2D and 3D.