Pulmonary nodule detection based on 3D multi-scale and semantic context heterogeneity

Yunchang Zhang; Xiaoqing Luo

doi:10.1117/12.2687875

28 August 2023 Pulmonary nodule detection based on 3D multi-scale and semantic context heterogeneity

Yunchang Zhang, Xiaoqing Luo

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272416 (2023) https://doi.org/10.1117/12.2687875
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

Automated lung nodule detection methods are challenged by the fact that pulmonary nodules often present multiple morphologies and scales and by the low differentiation of nodules from surrounding tissues. To address the above issues, we propose a pulmonary nodule detection method based on 3D multiscale and semantic context heterogeneity. The method uses dilated convolutions with different dilation rates to construct a Context Fusion Module (CFM) to extract pulmonary nodule multiscale fused features, thus enhancing the detection ability of non-significant and small nodules. The Channel Semantic Context Heterogeneity Model (CSHM) is proposed to address the problem of noise and redundant information in multi-scale fusion features, to suppress noise and redundant information from feature channel-level contextual relevance and redundancy, to enhance potential discriminative features, and to reduce the interference of useless information, so as to enhance the ability of nodule detection and localization. Experiments were conducted on a publicly available dataset (Luna Nodule Analysis 16, LUNA16) with a high sensitivity and an average sensitivity of 83.6% over eight false alarm numbers, which is better than the other method, validating the superiority of the proposed method.

Citation Download Citation

Yunchang Zhang and Xiaoqing Luo "Pulmonary nodule detection based on 3D multi-scale and semantic context heterogeneity", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272416 (28 August 2023); https://doi.org/10.1117/12.2687875

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KEYWORDS

3D modeling

Semantics

Feature fusion

Lung

Feature extraction

Convolution

Target detection

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