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Classification on smartphone-captured chest X-ray (CXR) photos to detectpathologies is challenging due to the projective transformation caused by thenon-ideal camera position. Recently, various rectification methods have beenproposed for different photo rectification tasks such as document photos,license plate photos, etc. Unfortunately, we found that none of them issuitable for CXR photos, due to their specific transformation type, imageappearance, annotation type, etc. In this paper, we propose an innovative deeplearning-based Projective Transformation Rectification Network (PTRN) toautomatically rectify CXR photos by predicting the projective transformationmatrix. To the best of our knowledge, it is the first work to predict theprojective transformation matrix as the learning goal for photo rectification.Additionally, to avoid the expensive collection of natural data, synthetic CXRphotos are generated under the consideration of natural perturbations, extrascreens, etc. We evaluate the proposed approach in the CheXphotosmartphone-captured CXR photos classification competition hosted by theStanford University Machine Learning Group, our approach won first place with ahuge performance improvement (ours 0.850, second-best 0.762, in AUC). A deeperstudy demonstrates that the use of PTRN successfully achieves theclassification performance on the spatially transformed CXR photos to the samelevel as on the high-quality digital CXR images, indicating PTRN can eliminateall negative impacts of projective transformation on the CXR photos.

Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification