论文标题
Chexnet精度改善骨骼阴影排除的上下文学习
Context Learning for Bone Shadow Exclusion in CheXNet Accuracy Improvement
论文作者
论文摘要
胸部X射线检查在肺部疾病检测中起重要作用。这项任务的准确性越准确，需要经验丰富的放射科医生。在发布了14个疾病的100,000多个额叶视图X射线图像之后，发布了几种模型，以高精度提出了几种模型。在本文中，我们开发了胸部X射线图像中肺部疾病诊断的工作流程，这可以将最新模型的平均AUROC从0.8414提高到0.8445。我们在进食14个疾病检测模型之前应用图像预处理步骤。我们的项目包括三个模型：第一个模型是Densenet-121，以预测处理的图像是否具有更好的结果，骨阴影排除的卷积自动编码器模型是第二个，最后一个是原始的Chexnet。
Chest X-ray examination plays an important role in lung disease detection. The more accuracy of this task, the more experienced radiologists are required. After ChestX-ray14 dataset containing over 100,000 frontal-view X-ray images of 14 diseases was released, several models were proposed with high accuracy. In this paper, we develop a work flow for lung disease diagnosis in chest X-ray images, which can improve the average AUROC of the state-of-the-art model from 0.8414 to 0.8445. We apply image preprocessing steps before feeding to the 14 diseases detection model. Our project includes three models: the first one is DenseNet-121 to predict whether a processed image has a better result, a convolutional auto-encoder model for bone shadow exclusion is the second one, and the last is the original CheXNet.
