ProCAN: Progressive growing channel attentive non-local network for lung nodule classification
ProCAN: Progressive growing channel attentive non-local network for lung nodule classification
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DOI：
10.1016/j.patcog.2021.108309
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发表时间：
2021-09-23
影响因子：
8
通讯作者：
Tan, Maxine
中科院分区：
文献类型：
--
作者：
Al-Shabi, Mundher;Shak, Kelvin;Tan, Maxine
肺癌的CT扫描分类是早期发现肺癌最重要的任务之一。如果我们能够准确地对恶性/癌性肺结节进行分类，就可以挽救许多生命。因此，最近提出了几种基于深度学习的模型来将肺结节分类为恶性或良性。然而，结节的大小和外观的巨大差异使这项任务极具挑战性。我们提出了一种新的渐进生长通道非局部（ProCAN）网络用于肺结节分类。所提出的方法从三个不同的方面解决了这一挑战。首先，我们通过在非本地网络中增加频道关注功能来丰富非本地网络。其次，我们应用课程学习原则，即我们首先在简单的例子上训练我们的模型，然后再训练难的例子。第三，随着课程学习过程中分类任务的难度增加，我们的模型逐渐成长，以提高其处理手头任务的能力。我们在两个不同的公共数据集上检验了我们提出的方法，并将其性能与文献中最先进的方法进行了比较。结果表明，ProCAN模型在LIDC-IDRI数据集上的AUC为98.05%，准确率为95.28%，优于现有方法。此外，我们进行了广泛的消融研究，以分析我们提出的方法的每个新组成部分的贡献和影响。(c) 2021 Elsevier Ltd.版权所有。
Lung cancer classification in screening computed tomography (CT) scans is one of the most crucial tasks for early detection of this disease. Many lives can be saved if we are able to accurately classify malignant/cancerous lung nodules. Consequently, several deep learning based models have been proposed recently to classify lung nodules as malignant or benign. Nevertheless, the large variation in the size and heterogeneous appearance of the nodules makes this task an extremely challenging one. We propose a new Progressive Growing Channel Attentive Non-Local (ProCAN) network for lung nodule classification. The proposed method addresses this challenge from three different aspects. First, we enrich the Non Local network by adding channel-wise attention capability to it. Second, we apply Curriculum Learning principles, whereby we first train our model on easy examples before hard ones. Third, as the classification task gets harder during the Curriculum learning, our model is progressively grown to increase its capability of handling the task at hand. We examined our proposed method on two different public datasets and compared its performance with state-of-the-art methods in the literature. The results show that the ProCAN model outperforms state-of-the-art methods and achieves an AUC of 98.05% and an accuracy of 95.28% on the LIDC-IDRI dataset. Moreover, we conducted extensive ablation studies to analyze the contribution and effects of each new component of our proposed method. (c) 2021 Elsevier Ltd. All rights reserved.