Attention-embedded complementary-stream CNN for false positive reduction in pulmonary nodule detection. (June 2021)

• Record Type: Journal Article
• Authors: Sun, Lingma; Wang, Zhuoran; Pu, Hong; Yuan, Guohui; Guo, Lu; Pu, Tian; Peng, Zhenming
• Is Part Of: Computers in biology and medicine. Volume 133(2021)
• Publication Date: 2021-06
• Subjects: Attention mechanism -- Convolutional neural network -- False positive reduction -- Pulmonary nodule detection -- Multi-scale features; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104357
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: False positive reduction plays a key role in computer-aided detection systems for pulmonary nodule detection in computed tomography (CT) scans. However, this remains a challenge owing to the heterogeneity and similarity of anisotropic pulmonary nodules. In this study, a novel attention-embedded complementary-stream convolutional neural network (AECS-CNN) is proposed to obtain more representative features of nodules for false positive reduction. The proposed network comprises three function blocks: 1) attention-guided multi-scale feature extraction, 2) complementary-stream block with an attention module for feature integration, and 3) classification block. The inputs of the network are multi-scale 3D CT volumes due to variations in nodule sizes. Subsequently, a gradual multi-scale feature extraction block with an attention module was applied to acquire more contextual information regarding the nodules. A subsequent complementary-stream integration block with an attention module was utilized to learn the significantly complementary features. Finally, the candidates were classified using a fully connected layer block. An exhaustive experiment on the LUNA16 challenge dataset was conducted to verify the effectiveness and performance of the proposed network. The AECS-CNN achieved a sensitivity of 0.92 with 4 false positives per scan. The results indicate that the attention mechanism can improve the network performance in false positive reduction, the proposed AECS-CNN can learn more representative features, and the attention module can guide the network to learn the discriminated feature channels and the crucial information embedded in the data, thereby effectively enhancing the performance of the detection system. Highlights: Attention-guided complementary-stream CNN can learn more contextual features of the nodules. CBAM can guide the network to retrieve the discriminated feature channels and crucial information from the nodule data. Embedded attention module can improve the network performance in FPR.