Fine-Grained Lung Cancer Classification from PET and CT Images Based on Multidimensional Attention Mechanism

• Published in: Complexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 12
• Main Authors: Yan, Bin, Shi, Dapeng, Chen, Jian, Hai, Jinjin, Qiao, Kai, Jiang, Lingyun, Wang, Zhenzhen, Qin, RuoXi, Xu, Junling
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 20.01.2020; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Cable television broadcasting industry; Cancer; Classification; Computed tomography; CT imaging; Deep learning; Diagnosis; Diagnostic imaging; Feature extraction; Image classification; Lung cancer; Machine learning; Medical imaging; Medical imaging equipment; Medical prognosis; Mortality; Noise; Noise control; Noise reduction; Oncology, Experimental; Positron emission; Rankings; Tomography; United States; Connecticut
• DOI: 10.1155/2020/6153657
• ISSN: 1076-2787

Lung cancer ranks among the most common types of cancer. Noninvasive computer-aided diagnosis can enable large-scale rapid screening of potential patients with lung cancer. Deep learning methods have already been applied for the automatic diagnosis of lung cancer in the past. Due to restrictions caused by single modality images of dataset as well as the lack of approaches that allow for a reliable extraction of fine-grained features from different imaging modalities, research regarding the automated diagnosis of lung cancer based on noninvasive clinical images requires further study. In this paper, we present a deep learning architecture that combines the fine-grained feature from PET and CT images that allow for the noninvasive diagnosis of lung cancer. The multidimensional (regarding the channel as well as spatial dimensions) attention mechanism is used to effectively reduce feature noise when extracting fine-grained features from each imaging modality. We conduct a comparative analysis of the two aspects of feature fusion and attention mechanism through quantitative evaluation metrics and the visualization of deep learning process. In our experiments, we obtained an area under the ROC curve of 0.92 (balanced accuracy = 0.72) and a more focused network attention which shows the effective extraction of the fine-grained feature from each imaging modality.