论文标题
通过增强训练管道的胸部X光片对胸部病理的自动鉴定
Automated Identification of Thoracic Pathology from Chest Radiographs with Enhanced Training Pipeline
论文作者
论文摘要
胸部X射线是诊断肺和心脏病的最常见放射学研究。因此，一种自动预先报告胸部X射线病理发现的系统将大大提高放射科医生的生产力。为此，我们通过新的训练方案研究了一个深入学习框架，以分类来自胸部X射线的不同胸病病理标签。我们使用目前最大的公开注释的数据集ChestX-Ray14，有112,120例胸部X光片，有30,805例患者。每个图像都用“ nofining”类或14个胸病病理标签中的一个或多个注释。受试者可以具有多种病理，从而导致多级，多标签问题。我们使用K-HOT编码将标签编码为二进制向量。我们研究了在ImageNet上预先训练的RESNET34体系结构，其中将两个关键的修改纳入了训练框架中：（1）具有动量的随机梯度下降，并使用余弦退火进行重新启动，（2）可变图像尺寸以进行微调以防止过度拟合。此外，我们使用启发式算法来选择良好的学习率。重新启动的学习用于避免局部最小值。接收器操作特性曲线（AUC）下的面积用于定量评估诊断质量。 Our results are comparable to, or outperform the best results of current state-of-the-art methods with AUCs as follows: Atelectasis:0.81, Cardiomegaly:0.91, Consolidation:0.81, Edema:0.92, Effusion:0.89, Emphysema: 0.92, Fibrosis:0.81, Hernia:0.84, Infiltration:0.73, Mass:0.85,结节：0.76，胸膜增厚：0.81，肺炎：0.77，肺炎：0.89和Nofinding：0.79。我们的结果表明，除了使用复杂的网络体系结构外，还可以提高学习率，调度程序和强大的优化器。
Chest x-rays are the most common radiology studies for diagnosing lung and heart disease. Hence, a system for automated pre-reporting of pathologic findings on chest x-rays would greatly enhance radiologists' productivity. To this end, we investigate a deep-learning framework with novel training schemes for classification of different thoracic pathology labels from chest x-rays. We use the currently largest publicly available annotated dataset ChestX-ray14 of 112,120 chest radiographs of 30,805 patients. Each image was annotated with either a 'NoFinding' class, or one or more of 14 thoracic pathology labels. Subjects can have multiple pathologies, resulting in a multi-class, multi-label problem. We encoded labels as binary vectors using k-hot encoding. We study the ResNet34 architecture, pre-trained on ImageNet, where two key modifications were incorporated into the training framework: (1) Stochastic gradient descent with momentum and with restarts using cosine annealing, (2) Variable image sizes for fine-tuning to prevent overfitting. Additionally, we use a heuristic algorithm to select a good learning rate. Learning with restarts was used to avoid local minima. Area Under receiver operating characteristics Curve (AUC) was used to quantitatively evaluate diagnostic quality. Our results are comparable to, or outperform the best results of current state-of-the-art methods with AUCs as follows: Atelectasis:0.81, Cardiomegaly:0.91, Consolidation:0.81, Edema:0.92, Effusion:0.89, Emphysema: 0.92, Fibrosis:0.81, Hernia:0.84, Infiltration:0.73, Mass:0.85, Nodule:0.76, Pleural Thickening:0.81, Pneumonia:0.77, Pneumothorax:0.89 and NoFinding:0.79. Our results suggest that, in addition to using sophisticated network architectures, a good learning rate, scheduler and a robust optimizer can boost performance.
