A transfer learning method with deep residual network for pediatric pneumonia diagnosis
A transfer learning method with deep residual network for pediatric pneumonia diagnosis
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DOI：
10.1016/j.cmpb.2019.06.023
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发表时间：
2020-04-01
影响因子：
6.1
通讯作者：
Zheng, Lixin
中科院分区：
文献类型：
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作者：
Liang, Gaobo;Zheng, Lixin
背景与目的：基于深度学习和医学影像的计算机辅助诊断系统日益成为研究热点。目前，经典的卷积神经网络是通过对原始图像进行分层抽象来产生分类结果的。这些抽象的特征对物体的位置和方向不太敏感，这种空间信息的缺乏限制了图像分类精度的进一步提高。因此，如何在临床实际应用中开发出合适的神经网络框架和训练策略来避免这一问题，是研究人员需要继续探索的课题。方法：提出一种结合残差思维和扩张卷积的深度学习框架，用于儿童肺炎的诊断和检测。具体而言，基于对儿童肺炎图像分类任务性质的理解，本文提出的方法利用残差结构克服了深度模型的过拟合和退化问题，利用展开卷积克服了模型深度增加导致的特征空间信息丢失问题。此外，为了克服由于数据不足导致模型训练困难以及引入结构化噪声对模型性能的负面影响，我们使用在同一领域的大规模数据集上学习到的模型参数，通过迁移学习对模型进行初始化。结果：我们提出的方法已被评估为提取与肺炎相关的纹理特征，并准确识别图像中最能指示肺炎的区域的性能。测试数据集的实验结果表明，该方法对儿童肺炎分类任务的召回率为96.7%，fl-score为92.7%。与现有技术方法相比，该方法可有效解决儿童胸部x线图像图像分辨率低、炎症区部分闭塞的问题。结论：新框架侧重于应用直接进行病灶表征的高级分类，在儿童肺炎的分类任务中具有较高的可靠性。(C) 2019 Elsevier B.V.版权所有
Background and Objective: Computer aided diagnosis systems based on deep learning and medical imaging is increasingly becoming research hotspots. At the moment, the classical convolutional neural network generates classification results by hierarchically abstracting the original image. These abstract features are less sensitive to the position and orientation of the object, and this lack of spatial information limits the further improvement of image classification accuracy. Therefore, how to develop a suitable neural network framework and training strategy in practical clinical applications to avoid this problem is a topic that researchers need to continue to explore.Methods: We propose a deep learning framework that combines residual thought and dilated convolution to diagnose and detect childhood pneumonia. Specifically, based on an understanding of the nature of the child pneumonia image classification task, the proposed method uses the residual structure to overcome the over-fitting and the degradation problems of the depth model, and utilizes dilated convolution to overcome the problem of loss of feature space information caused by the increment in depth of the model. Furthermore, in order to overcome the problem of difficulty in training model due to insufficient data and the negative impact of the introduction of structured noise on the performance of the model, we use the model parameters learned on large-scale datasets in the same field to initialize our model through transfer learning.Results: Our proposed method has been evaluated for extracting texture features associated with pneumonia and for accurately identifying the performance of areas of the image that best indicate pneumonia. The experimental results of the test dataset show that the recall rate of the method on children pneumonia classification task is 96.7%, and the fl-score is 92.7%. Compared with the prior art methods, this approach can effectively solve the problem of low image resolution and partial occlusion of the inflammatory area in children chest X-ray images.Conclusions: The novel framework focuses on the application of advanced classification that directly performs lesion characterization, and has high reliability in the classification task of children pneumonia. (C) 2019 Elsevier B.V. All rights reserved.