Early diagnosis of interstitial lung diseases is crucial for their treatment,
but even experienced physicians find it difficult, as their clinical
manifestations are similar. In order to assist with the diagnosis,
computer-aided diagnosis (CAD) systems have been developed. These commonly rely
on a fixed scale classifier that scans CT images, recognizes textural lung
patterns and generates a map of pathologies. In a previous study, we proposed a
method for classifying lung tissue patterns using a deep convolutional neural
network (CNN), with an architecture designed for the specific problem. In this
study, we present an improved method for training the proposed network by
transferring knowledge from the similar domain of general texture
classification. Six publicly available texture databases are used to pretrain
networks with the proposed architecture, which are then fine-tuned on the lung
tissue data. The resulting CNNs are combined in an ensemble and their fused
knowledge is compressed back to a network with the original architecture. The
proposed approach resulted in an absolute increase of about 2% in the
performance of the proposed CNN. The results demonstrate the potential of
transfer learning in the field of medical image analysis, indicate the textural
nature of the problem and show that the method used for training a network can
be as important as designing its architecture.

应用迁移学习技术，通过纹理数据库来改进深度卷积神经网络的训练方法，并将不同卷积神经网络中的知识结合，进而实现肺组织病变的分类，该方法的成功应用表明了在医学图像分析领域中，训练神经网络的方法同样重要于设计它的架构。