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3D multi-scale vision transformer for lung nodule detection in chest CT images

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Abstract

Lung cancer becomes the most prominent cause of cancer-related death in society. Normally, radiologists use computed tomography (CT) to diagnose lung nodules in lung cancer patients. A single CT scan for a patient produces hundreds of images that are manually analyzed by radiologists which is a big burden and sometimes leads to inaccuracy. Recently, many computer-aided diagnosis (CAD) systems integrated with deep learning architectures have been proposed to assist radiologists. This study proposes the CAD scheme based on a 3D multi-scale vision transformer (3D-MSViT) to enhance multi-scale feature extraction and improves lung nodule prediction efficiency from 3D CT images. The 3D-MSViT architecture adopted a local–global transformer block structure whereby the local transformer stage individually processes each scale patch and forwards it to the global transformer level for merging multi-scale features. The transformer blocks fully relied on the attention mechanism without the inclusion of the convolutional neural network to reduce the network parameters. The proposed CAD scheme was validated on 888 CT images of the Lung Nodule Analysis 2016 (LUNA16) public dataset. Free-response receiver operating characteristics analysis was adopted to evaluate the proposed method. The 3D-MSViT algorithm obtained the highest sensitivity of 97.81% and competition performance metrics of 0.911. Therefore, the 3D-MSViT scheme obtained comparable results with low network complexity related to the counterpart deep learning approaches in prior studies.

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Availability of data and materials

The research work uses the LUNA16 dataset that is available online through https://luna16.grand-challenge.org/.

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Acknowledgements

The authors would like to thank the Editors and synonyms reviewers for their constructive comments on improving this work.

Funding

This work is supported by The National Natural Science Foundation of China under Grant Numbers 61671185 and 62071153.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Hassan Mkindu, Longwen Wu & Yaqin Zhao

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  1. Hassan Mkindu
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  2. Longwen Wu
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Contributions

HM: Methodology, formal analysis, and writing—original draft preparation. LW: The manuscript investigation, writing—review, and editing. YZ: Conceptualization, resources, funding acquisition, and supervision.

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Correspondence to Longwen Wu.

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Mkindu, H., Wu, L. & Zhao, Y. 3D multi-scale vision transformer for lung nodule detection in chest CT images. SIViP 17, 2473–2480 (2023). https://doi.org/10.1007/s11760-022-02464-0

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  • DOI: https://doi.org/10.1007/s11760-022-02464-0

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