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An Embedded Multi-branch 3D Convolution Neural Network for False Positive Reduction in Lung Nodule Detection
Journal of Digital Imaging ( IF 4.4 ) Pub Date : 2020-02-24 , DOI: 10.1007/s10278-020-00326-0
Wangxia Zuo 1, 2 , Fuqiang Zhou 1 , Yuzhu He 1
Affiliation  

Abstract

Numerous lung nodule candidates can be produced through an automated lung nodule detection system. Classifying these candidates to reduce false positives is an important step in the detection process. The objective during this paper is to predict real nodules from a large number of pulmonary nodule candidates. Facing the challenge of the classification task, we propose a novel 3D convolution neural network (CNN) to reduce false positives in lung nodule detection. The novel 3D CNN includes embedded multiple branches in its structure. Each branch processes a feature map from a layer with different depths. All of these branches are cascaded at their ends; thus, features from different depth layers are combined to predict the categories of candidates. The proposed method obtains a competitive score in lung nodule candidate classification on LUNA16 dataset with an accuracy of 0.9783, a sensitivity of 0.8771, a precision of 0.9426, and a specificity of 0.9925. Moreover, a good performance on the competition performance metric (CPM) is also obtained with a score of 0.830. As a 3D CNN, the proposed model can learn complete and three-dimensional discriminative information about nodules and non-nodules to avoid some misidentification problems caused due to lack of spatial correlation information extracted from traditional methods or 2D networks. As an embedded multi-branch structure, the model is also more effective in recognizing the nodules of various shapes and sizes. As a result, the proposed method gains a competitive score on the false positive reduction in lung nodule detection and can be used as a reference for classifying nodule candidates.



中文翻译:

用于减少肺结节检测误报的嵌入式多分支 3D 卷积神经网络

摘要

可以通过自动肺结节检测系统产生大量肺结节候选物。对这些候选对象进行分类以减少误报是检测过程中的一个重要步骤。本文的目标是从大量肺结节候选者中预测真正的结节。面对分类任务的挑战,我们提出了一种新颖的 3D 卷积神经网络 (CNN),以减少肺结节检测中的误报。新颖的 3D CNN 在其结构中嵌入了多个分支。每个分支处理来自具有不同深度的层的特征图。所有这些分支都在其末端级联;因此,组合来自不同深度层的特征来预测候选的类别。所提出的方法在LUNA16数据集上获得了肺结节候选分类的竞争分数,准确度为0.9783,灵敏度为0.8771,准确度为0.9426,特异性为0.9925。此外,在竞争性能指标 (CPM) 上也获得了良好的表现,得分为 0.830。作为一个3D CNN,所提出的模型可以学习关于结节和非结节的完整的三维判别信息,以避免由于缺乏从传统方法或2D网络中提取的空间相关信息而导致的一些错误识别问题。作为嵌入式多分支结构,该模型在识别各种形状和大小的结节方面也更加有效。其结果,

更新日期:2020-03-07
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