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Multi-Task Learning for Lung Nodule Classification on Chest CT
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DOI：10.1109/ACCESS.2020.3027812.png)
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摘要
En 中文
Lung cancer is one of the leading causes of death over the world. Detecting and identifying malignant nodules on chest computed tomography (CT) plays an important role in the diagnosis and treatment of lung cancer. Computer-aided diagnosis (CAD) systems have been developed to identify lung nodules. However, the problem of a high false positive rate is still not well solved. In this paper, we propose a novel multi-task convolutional neural network (MT-CNN) framework to identify malignant nodules from benign nodules on chest CT scans. MT-CNN learns three-dimensional (3-D) lung nodule characteristics from nine two-dimensional (2-D) views, which are decomposed from different angles of each nodule. Each of 2-D MT-CNN model consists of two branches, one is the nodule classification branch (main task) and the other is the image reconstruction branch (auxiliary task). The motivation of the auxiliary task is to preserve more microscopic information in the hierarchical structure of CNN, which is beneficial to malignant nodule identification. The final classification result is obtained by integrating nine 2-D models. We test our method on the benchmark LUNA-16 and LIDC-IDRI datasets and compare it with state-of-the-art models. MT-CNN achieves the lowest false positive rate (3.2%) and highest AUC (97.3%) in LUNA-16 dataset and achieves an AUC of 95.59% in LIDC-IDRI. These results demonstrate the advantage of our method.
Keyword:
Lung
Cancer
Computed tomography
Solid modeling
Task analysis
Image reconstruction
Computational modeling
Multi-task learning
lung nodule classification
image reconstruction
computer-aided diagnosis
convolutional neural network
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge计算机断层扫描图像中自动检测肺结节的算法的验证，比较和组合: LUNA16挑战.png)
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OAAI分享收藏A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classificationMEDICAL IMAGE ANALYSISOAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAI分享收藏Detection and Classification of Pulmonary Nodules Using Convolutional Neural Networks: A Survey使用卷积神经网络检测和分类肺结节: 一项调查IEEE ACCESSOAAI
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