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首页> 外文期刊>Medical and Biological Engineering and Computing: Journal of the International Federation for Medical and Biological Engineering >A deep convolutional neural network architecture for interstitial lung disease pattern classification
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A deep convolutional neural network architecture for interstitial lung disease pattern classification

机译：一种深度卷积神经网络架构，适用于间质肺疾病模式分类

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摘要

Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity among the clinical manifestations of these diseases. In order to assist the radiologists, computer-aided diagnosis systems have been developed. Besides, the potential of deep convolutional neural networks (CNNs) is also expected to exert on the medical image analysis in recent years. In this paper, we design a new deep convolutional neural network (CNN) architecture to achieve the classification task of ILD patterns. Furthermore, we also propose a novel two-stage transfer learning (TSTL) method to deal with the problem of the lack of training data, which leverages the knowledge learned from sufficient textural source data and auxiliary unlabeled lung CT data to the target domain. We adopt the unsupervised manner to learn the unlabeled data, by which the objective function composed of the prediction confidence and mutual information are optimized. The experimental results show that our proposed CNN architecture achieves desirable performance and outperforms most of the state-of-the-art ones. The comparative analysis demonstrates the promising feasibility and advantages of the proposed two-stage transfer learning strategy as well as the potential of the knowledge learning from lung CT data.
机译：间质肺病（ILD）是指肺组织的各种异常炎症，这些疾病模式的早期诊断对于治疗至关重要。然而，由于这些疾病的临床表现之间的相似性，难以做出准确的诊断。为了帮助放射科医师，已经开发了计算机辅助诊断系统。此外，近年来，还预期深度卷积神经网络（CNNS）的潜力也将施加对医学图像分析。在本文中，我们设计了一种新的深度卷积神经网络（CNN）架构，以实现ILD模式的分类任务。此外，我们还提出了一种新颖的两阶段转移学习（TSTL）方法来处理缺乏培训数据的问题，它利用从足够的纹理源数据和辅助未标记的肺CT数据到目标域中学习的知识。我们采用无人监督的方式来学习未标记的数据，由其优化了由预测置信度和互信息组成的目标函数。实验结果表明，我们提出的CNN架构实现了所需的性能和优于大多数最先进的结构。比较分析表明，提出的两级转移学习策略以及从肺CT数据的知识潜力的潜力的有希望的可行性和优势。

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