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Multiscale CNN with compound fusions for false positive reduction in lung nodule detection
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2021-01-12 , DOI: 10.1016/j.artmed.2021.102017
Pardha Saradhi Mittapalli 1 , Thanikaiselvan V 2
Affiliation  

Pulmonary lung nodules are often benign at the early stage but they could easily become malignant and metastasize to other locations in later stages. Morphological characteristics of these nodule instances vary largely in terms of their size, shape, and texture. There are also other co-existing lung anatomical structures such as lung walls and blood vessels surrounding these nodules resulting in complex contextual information. As a result, their early diagnosis to enable decisive intervention using Computer-Aided Diagnosis (CAD) systems face serious challenges, especially at low false positive rates. In this paper, we propose a new Convolutional Neural Network (CNN) architecture called Multiscale CNN with Compound Fusions (MCNN-CF) for this purpose which uses multiscale 3D patches as inputs and performs a fusion of intermediate features at two different depths of the network in two diverse fashions. The network is trained by a new iterative training procedure adapted to circumvent the class imbalance problem and obtained a Competitive Performance Metric (CPM) score of 0.948 when tested on the LUNA16 dataset. Experimental results illustrate the robustness of the proposed system which has increased the confidence of the prediction probabilities in the detection of the most variety of nodules.



中文翻译:

具有复合融合的多尺度 CNN 用于减少肺结节检测的假阳性

肺结节早期通常是良性的,但后期很容易恶​​变并转移到其他部位。这些结核实例的形态特征在其大小、形状和质地方面差异很大。还有其他共存的肺解剖结构,例如围绕这些结节的肺壁和血管,导致复杂的上下文信息。因此,他们使用计算机辅助诊断 (CAD) 系统进行早期诊断以实现果断干预面临严峻挑战,尤其是在低误报率的情况下。在这篇论文中,为此,我们提出了一种新的卷积神经网络 (CNN) 架构,称为具有复合融合的多尺度 CNN (MCNN-CF),它使用多尺度 3D 补丁作为输入,并以两种不同的方式在网络的两个不同深度执行中间特征的融合. 该网络通过新的迭代训练程序进行训练,该程序适用于规避类不平衡问题,并在 LUNA16 数据集上进行测试时获得了 0.948 的竞争性能指标 (CPM) 分数。实验结果说明了所提出的系统的鲁棒性,它增加了在检测最多种结节时预测概率的置信度。该网络通过新的迭代训练程序进行训练,该程序适用于规避类不平衡问题,并在 LUNA16 数据集上进行测试时获得了 0.948 的竞争性能指标 (CPM) 分数。实验结果说明了所提出的系统的鲁棒性,它增加了在检测最多种结节时预测概率的置信度。该网络通过新的迭代训练程序进行训练,该程序适用于规避类不平衡问题,并在 LUNA16 数据集上进行测试时获得了 0.948 的竞争性能指标 (CPM) 分数。实验结果说明了所提出的系统的鲁棒性,它增加了在检测最多种结节时预测概率的置信度。

更新日期:2021-02-01
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