  Chest X-ray (CXR) is the most common examination for fast detection of
pulmonary abnormalities. Recently, automated algorithms have been developed to
classify multiple diseases and abnormalities in CXR scans. However, because of
the limited availability of scans containing nodules and the subtle properties
of nodules in CXRs, state-of-the-art methods do not perform well on nodule
classification. To create additional data for the training process, standard
augmentation techniques are applied. However, the variance introduced by these
methods are limited as the images are typically modified globally. In this
paper, we propose a method for local feature augmentation by extracting local
nodule features using a generative inpainting network. The network is applied
to generate realistic, healthy tissue and structures in patches containing
nodules. The nodules are entirely removed in the inpainted representation. The
extraction of the nodule features is processed by subtraction of the inpainted
patch from the nodule patch. With arbitrary displacement of the extracted
nodules in the lung area across different CXR scans and further local
modifications during training, we significantly increase the nodule
classification performance and outperform state-of-the-art augmentation
methods.
