论文标题
Y-NET用于胸部X射线预处理：同时分类几何和注释分割
Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations
论文作者
论文摘要
在过去的十年中，卷积神经网络（CNN）已成为图像分类和分割的领先算法。最近发布的大型医学成像数据库已加速了它们在生物医学领域的使用。尽管训练图片分类的数据受益于积极的几何增强，但医学诊断（尤其是在胸部X光片）中更大程度地取决于特征位置。诊断分类结果可以通过依赖射线照相注释来人为地增强。这项工作介绍了胸部X射线输入到机器学习算法中的一般预处理步骤。基于VGG11编码器的修改后的Y-NET体系结构用于同时学习胸部的几何定向（相似性变换参数）和放射线学注释的分割。胸部X射线是从已发布的数据库中获得的。该算法接受了1000个手动标记图像的培训。结果由专业临床医生评估，可接受的几何形状为95.8％，注释蒙版为96.2％（n = 500），而对照图像分别为27.0％和34.9％（n = 241）。我们假设这一预处理步骤将改善未来诊断算法的鲁棒性。
Over the last decade, convolutional neural networks (CNNs) have emerged as the leading algorithms in image classification and segmentation. Recent publication of large medical imaging databases have accelerated their use in the biomedical arena. While training data for photograph classification benefits from aggressive geometric augmentation, medical diagnosis -- especially in chest radiographs -- depends more strongly on feature location. Diagnosis classification results may be artificially enhanced by reliance on radiographic annotations. This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation (similarity transform parameters) of the chest and segmentation of radiographic annotations. Chest x-rays were obtained from published databases. The algorithm was trained with 1000 manually labeled images with augmentation. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2% (n=500), compared to 27.0% and 34.9% respectively in control images (n=241). We hypothesize that this pre-processing step will improve robustness in future diagnostic algorithms.
