论文标题
图像投影转换纠正与智能手机捕获胸部X射线照片分类的合成数据
Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification
论文作者
论文摘要
智能手机捕获的胸部X射线（CXR）照片的分类是由于非理想摄像机位置引起的投射转换，这是具有挑战性的。 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.据我们所知，这是第一项预测投射转换矩阵作为照片纠正目标的工作。此外，为了避免自然数据的昂贵收集，合成CXR照片是在自然扰动，额外的屏幕等考虑下生成的。我们评估了斯坦福大学机器学习组主办的Chexphoto智能手机捕获的CXR捕获的CXR照片分类竞赛，我们的方法获得了巨大的绩效，我们的方法获得了巨大的绩效（我们的巨大的绩效提高（我们的0.850 0.850 best-best-best-best auccess a a a）。一项更深入的研究表明，PTRN的使用成功地在空间转换的CXR照片上达到了与高质量数字CXR图像相同的水平，这表明PTRN可以消除投影对CXR照片的投射转换的所有负面影响。
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.
