  Chest radiographs are commonly performed low-cost exams for screening and
diagnosis. However, radiographs are 2D representations of 3D structures causing
considerable clutter impeding visual inspection and automated image analysis.
Here, we propose a Fully Convolutional Network to suppress, for a specific
task, undesired visual structure from radiographs while retaining the relevant
image information such as lung-parenchyma. The proposed algorithm creates
reconstructed radiographs and ground-truth data from high resolution CT-scans.
Results show that removing visual variation that is irrelevant for a
classification task improves the performance of a classifier when only limited
training data are available. This is particularly relevant because a low number
of ground-truth cases is common in medical imaging.
