Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder.
Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder.
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基于迁移学习和卷积稀疏去噪自动编码器混合模型的自动胸部筛查
DOI：
10.1186/s12938-018-0496-2
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发表时间：
2018-05-23
影响因子：
3.9
通讯作者：
Hu Q
中科院分区：
文献类型：
--
作者：
Wang C;Elazab A;Jia F;Wu J;Hu Q
在本文中，我们的目的是调查计算机辅助分诊系统的效果，该系统用于肺部病变的健康检查，涉及诊断所需的数万张胸部X光片（CXR）。因此，通过自动化系统的诊断的高准确性可以减少放射科医生的工作量上仔细检查的医学images.MethodWe提出了一个深度学习模型，以有效地检测异常水平或识别正常水平在大规模胸部筛查，以获得CXR的概率置信度。此外，卷积稀疏去噪自动编码器的设计计算重建误差。我们采用了四个公开的放射学数据集有关的CXR，分析他们的报告，并利用他们的图像挖掘正确的疾病水平的CXR提交给计算机辅助分诊系统。基于我们的方法，我们投票的最终决定，从多分类器，以确定这三个级别的图像（即正常，异常，和不确定的情况下），CXRs falling.ResultsWe只处理体检的等级诊断，并提出多个新的度量指标。通过使用接收器操作特征曲线下的面积组合用于分类的预测因子，我们观察到最终决策与来自重建误差的阈值和概率值有关。我们的方法取得了可喜的成果，在98.7%和94.3%的精度的基础上正常和异常cases.ConclusionThe所提出的框架实现的结果显示出优越性，在疾病水平进行分类，具有较高的准确性。这可以潜在地节省放射科医生的时间和精力，以便使他们能够专注于更高级别的风险CXR。
ObjectiveIn this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist’s workload on scrutinizing the medical images.MethodWe present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into.ResultsWe only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively.ConclusionThe results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs.
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影响因子：
4.6
作者：
Dou, Qi;Chen, Hao;Heng, Pheng-Ann
通讯作者：
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影响因子：
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影响因子：
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作者：
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DOI：
10.1109/tpami.2017.2699184
发表时间：
2018-04-01
影响因子：
23.6
作者：
Chen, Liang-Chieh;Papandreou, George;Yuille, Alan L.
通讯作者：
Yuille, Alan L.
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影响因子：
10.6
作者：
Chen, Sihong;Qin, Jing;Cheng, Jie-Zhi
通讯作者：
Cheng, Jie-Zhi