  Classification on smartphone-captured chest X-ray (CXR) photos to detect
pathologies is challenging due to the projective transformation caused by the
non-ideal camera position. Recently, various rectification methods have been
proposed for different photo rectification tasks such as document photos,
license plate photos, etc. Unfortunately, we found that none of them is
suitable for CXR photos, due to their specific transformation type, image
appearance, annotation type, etc. In this paper, we propose an innovative deep
learning-based Projective Transformation Rectification Network (PTRN) to
automatically rectify CXR photos by predicting the projective transformation
matrix. To the best of our knowledge, it is the first work to predict the
projective transformation matrix as the learning goal for photo rectification.
Additionally, to avoid the expensive collection of natural data, synthetic CXR
photos are generated under the consideration of natural perturbations, extra
screens, etc. We evaluate the proposed approach in the CheXphoto
smartphone-captured CXR photos classification competition hosted by the
Stanford University Machine Learning Group, our approach won first place with a
huge performance improvement (ours 0.850, second-best 0.762, in AUC). A deeper
study demonstrates that the use of PTRN successfully achieves the
classification performance on the spatially transformed CXR photos to the same
level as on the high-quality digital CXR images, indicating PTRN can eliminate
all negative impacts of projective transformation on the CXR photos.
