论文标题
胸部X光片上使用密集的卷积网络对胸部疾病的多标签分类
Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs
论文作者
论文摘要
识别X射线图像中病理学的传统方法在很大程度上取决于熟练的人类解释，并且经常耗时。深度学习技术的出现使自动疾病诊断系统的发展。尽管如此，此类系统的性能是最终用户不透明的，并且仅限于检测单一病理。在本文中，我们提出了一个多标签疾病预测模型，该模型允许在给定的测试时间检测多种病理。我们使用密集的卷积神经网络（Densenet）进行疾病诊断。我们提出的模型的适用性为0.826的条件心脏肿瘤的最高AUC得分为0.896，而Nodule的AUC得分为0.655，精度为0.66。为了建立对决策的信任，我们在X射线上生成了热图，以可视化模型注意以做出某些预测的区域。我们提出的自动化疾病预测模型在多标签疾病预测任务中获得了高度自信的高性能指标。
Traditional methods of identifying pathologies in X-ray images rely heavily on skilled human interpretation and are often time-consuming. The advent of deep learning techniques has enabled the development of automated disease diagnosis systems. Still, the performance of such systems is opaque to end-users and limited to detecting a single pathology. In this paper, we propose a multi-label disease prediction model that allows the detection of more than one pathology at a given test time. We use a dense convolutional neural network (DenseNet) for disease diagnosis. Our proposed model achieved the highest AUC score of 0.896 for the condition Cardiomegaly with an accuracy of 0.826, while the lowest AUC score was obtained for Nodule, at 0.655 with an accuracy of 0.66. To build trust in decision-making, we generated heatmaps on X-rays to visualize the regions where the model paid attention to make certain predictions. Our proposed automated disease prediction model obtained highly confident high-performance metrics in multi-label disease prediction tasks.
