Multi-scale Convolutional Neural Networks for Lung Nodule Classification.
Multi-scale Convolutional Neural Networks for Lung Nodule Classification.
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DOI：
10.1007/978-3-319-19992-4_46
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发表时间：
2015-01-01
期刊：
影响因子：
--
通讯作者：
Tian, Jie
中科院分区：
文献类型：
--
作者：
Shen, Wei;Zhou, Mu;Tian, Jie
我们研究了使用胸部计算机断层扫描(CT)筛查诊断肺结节分类的问题。与传统研究主要依赖于结节分割进行区域分析不同，我们解决了一个更具挑战性的问题，即在没有任何预先定义的结节形态的情况下直接对原始结节块进行建模。我们提出了一种分层学习框架--多尺度卷积神经网络(MCNN)--通过从交替堆积的层中提取区分特征来捕捉结节的异质性。特别是，为了充分量化结节特征，我们的框架利用多尺度结节斑块来同时学习一组特定于类的特征，方法是将最后一层从每个输入尺度获得的响应神经元激活串联起来。我们对来自肺部图像数据库联盟和图像数据库资源倡议(LIDC-IDRI)的CT图像进行了评估，其中提供了肺结节筛查和结节注释。实验结果表明，该方法在不分割结节的情况下，对结节的良恶性分类是有效的。
We investigate the problem of diagnostic lung nodule classification using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on nodule segmentation for regional analysis, we tackle a more challenging problem on directly modelling raw nodule patches without any prior definition of nodule morphology. We propose a hierarchical learning framework--Multi-scale Convolutional Neural Networks (MCNN)--to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. In particular, to sufficiently quantify nodule characteristics, our framework utilizes multi-scale nodule patches to learn a set of class-specific features simultaneously by concatenating response neuron activations obtained at the last layer from each input scale. We evaluate the proposed method on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), where both lung nodule screening and nodule annotations are provided. Experimental results demonstrate the effectiveness of our method on classifying malignant and benign nodules without nodule segmentation.