论文标题
X射线图像的端到端深诊断
End-to-End Deep Diagnosis of X-ray Images
论文作者
论文摘要
在这项工作中，我们为X射线图像诊断提供了一个端到端的深度学习框架。作为第一步，我们的系统确定提交的图像是否为X射线。在对X射线的类型进行分类后，它运行了专用的异常分类网络。在这项工作中，我们只专注于胸部X射线射线，以进行异常分类。但是，该系统可以轻松扩展到其他X射线类型。我们的深度学习分类器基于Densenet-121体系结构。对于“ X射线与否”，“ X射线类型分类”和“胸部异常分类”任务的测试集精度分别为0.987、0.976和0.947，导致端到端精度为0.91。为了获得比“胸部异常分类”中最新的结果更好的结果，我们利用了新的RADAM优化器。我们还使用梯度加权的类激活映射来视觉解释结果。我们的结果表明，广义在线投影射线照相诊断系统的可行性。
In this work, we present an end-to-end deep learning framework for X-ray image diagnosis. As the first step, our system determines whether a submitted image is an X-ray or not. After it classifies the type of the X-ray, it runs the dedicated abnormality classification network. In this work, we only focus on the chest X-rays for abnormality classification. However, the system can be extended to other X-ray types easily. Our deep learning classifiers are based on DenseNet-121 architecture. The test set accuracy obtained for 'X-ray or Not', 'X-ray Type Classification', and 'Chest Abnormality Classification' tasks are 0.987, 0.976, and 0.947, respectively, resulting into an end-to-end accuracy of 0.91. For achieving better results than the state-of-the-art in the 'Chest Abnormality Classification', we utilize the new RAdam optimizer. We also use Gradient-weighted Class Activation Mapping for visual explanation of the results. Our results show the feasibility of a generalized online projectional radiography diagnosis system.
