A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation
A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation
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DOI：
10.1109/jbhi.2018.2879834
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发表时间：
2019-05-01
影响因子：
7.7
通讯作者：
Qin, Hong
中科院分区：
文献类型：
--
作者：
Sahu, Pranjal;Yu, Dantong;Qin, Hong
结节的大小和形状是诊断肺癌恶性程度的重要指标。然而，在计算机辅助系统中从 CT 扫描中有效捕获结节的结构信息是一项具有挑战性的任务。与之前提出计算密集型深度集成模型或三维 CNN 模型的模型不同，我们提出了一种轻量级、基于多视图采样的多部分 CNN 架构。该模型从多个视角获取结节的横截面，并通过视图池层聚合来自结节不同横截面的信息，将结节的体积信息编码为紧凑的表示。紧凑特征随后用于结节分类任务。该方法不需要结节的空间注释，并且直接作用于从包围结节的体积生成的横截面。我们在肺部图像数据库联盟（LIDC）和图像数据库资源倡议（IDRI）数据集上评估了所提出的方法。它实现了最先进的性能，平均分类准确率为 93.18%。该架构还可用于选择代表性横截面，确定结节的恶性程度，从而有助于结果的解释。由于重量轻，该模型可以移植到移动设备，从而将人工智能（AI）驱动的应用程序的力量直接带到从业者手中。
The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous models that proposed computationally intensive deep ensemble models or three-dimensional CNN models, we propose a lightweight, multiple view sampling based multi-section CNN architecture. The model obtains a nodule's cross sections from multiple view angles and encodes the nodule's volumetric information into a compact representation by aggregating information from its different cross sections via a view pooling layer. The compact feature is subsequently used for the task of nodule classification. The method does not require the nodule's spatial annotation and works directly on the cross sections generated from volume enclosing the nodule. We evaluated the proposed method on lung image database consortium (LIDC) and image database resource initiative (IDRI) dataset. It achieved the state-of-the-art performance with a mean 93.18% classification accuracy. The architecture could also be used to select the representative cross sections determining the nodule's malignancy that facilitates in the interpretation of results. Because of being lightweight, the model could be ported to mobile devices, which brings the power of artificial intelligence (AI) driven application directly into the practitioner's hand.