基于胸部X光片与密集卷积网络的多标签胸部疾病分类
Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs
AI总结：
本文提出基于DenseNet的多标签胸部疾病预测模型，可同时检测多种病理，并通过热力图可视化决策依据，在心脏肥大等疾病上取得较高AUC和准确率。
摘要：
传统的X射线图像病理识别方法高度依赖熟练的人工判读，且往往耗时较长。深度学习技术的出现推动了自动化疾病诊断系统的发展。然而，此类系统的性能对最终用户而言并不透明，并且仅限于检测单一病理。在本文中，我们提出了一种多标签疾病预测模型，能够在给定测试时刻检测一种以上的病理。我们使用密集卷积神经网络（DenseNet）进行疾病诊断。我们提出的模型在心脏肥大这一疾病上取得了最高的AUC分数0.896，准确率为0.826；而在结节上取得的AUC分数最低，为0.655，准确率为0.66。为了在决策过程中建立信任，我们在X光片上生成热力图，以可视化模型做出特定预测时所关注的区域。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取得了高置信度的高性能指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
