基于合成数据的图像投影变换校正用于智能手机拍摄的胸部X光照片分类
Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification
AI总结：
针对智能手机拍摄的胸部X光照片因投影变换导致分类困难，提出基于深度学习的投影变换校正网络（PTRN），预测变换矩阵自动校正，在CheXphoto竞赛中以AUC 0.850获第一，消除变换负面影响。
摘要：
对智能手机拍摄的胸部X光（CXR）照片进行分类以检测病理具有挑战性，因为非理想相机位置会导致投影变换。最近，针对不同的照片校正任务（如文档照片、车牌照片等）提出了各种校正方法。不幸的是，我们发现它们都不适用于CXR照片，因为其特定的变换类型、图像外观、标注类型等。在本文中，我们提出了一种创新的基于深度学习的投影变换校正网络（PTRN），通过预测投影变换矩阵来自动校正CXR照片。据我们所知，这是首个将预测投影变换矩阵作为照片校正学习目标的工作。此外，为了避免昂贵的自然数据收集，我们在考虑自然扰动、额外屏幕等因素下生成了合成CXR照片。我们在斯坦福大学机器学习小组主办的CheXphoto智能手机拍摄CXR照片分类竞赛中评估了所提出的方法，我们的方法以巨大的性能提升获得了第一名（我们的AUC为0.850，第二名最佳为0.762）。更深入的研究表明，使用PTRN成功地将空间变换的CXR照片的分类性能提升到了与高质量数字CXR图像相同的水平，表明PTRN可以消除投影变换对CXR照片的所有负面影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
