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.
@article{arxiv.2002.00751,
title = {Separation of target anatomical structure and occlusions in chest radiographs},
author = {Johannes Hofmanninger and Sebastian Roehrich and Helmut Prosch and Georg Langs},
journal= {arXiv preprint arXiv:2002.00751},
year = {2020}
}