TiCNet : Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images
© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine..
Lung cancer is the leading cause of cancer death. Since lung cancer appears as nodules in the early stage, detecting the pulmonary nodules in an early phase could enhance the treatment efficiency and improve the survival rate of patients. The development of computer-aided analysis technology has made it possible to automatically detect lung nodules in Computed Tomography (CT) screening. In this paper, we propose a novel detection network, TiCNet. It is attempted to embed a transformer module in the 3D Convolutional Neural Network (CNN) for pulmonary nodule detection on CT images. First, we integrate the transformer and CNN in an end-to-end structure to capture both the short- and long-range dependency to provide rich information on the characteristics of nodules. Second, we design the attention block and multi-scale skip pathways for improving the detection of small nodules. Last, we develop a two-head detector to guarantee high sensitivity and specificity. Experimental results on the LUNA16 dataset and PN9 dataset showed that our proposed TiCNet achieved superior performance compared with existing lung nodule detection methods. Moreover, the effectiveness of each module has been proven. The proposed TiCNet model is an effective tool for pulmonary nodule detection. Validation revealed that this model exhibited excellent performance, suggesting its potential usefulness to support lung cancer screening.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
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| Enthalten in: |
Journal of imaging informatics in medicine - 37(2024), 1 vom: 12. Feb., Seite 196-208 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ma, Ling [VerfasserIn] |
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| Links: |
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| Themen: |
Attention mechanism |
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| Anmerkungen: |
Date Completed 04.03.2024 Date Revised 29.03.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s10278-023-00904-y |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368333612 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine. | ||
| 520 | |a Lung cancer is the leading cause of cancer death. Since lung cancer appears as nodules in the early stage, detecting the pulmonary nodules in an early phase could enhance the treatment efficiency and improve the survival rate of patients. The development of computer-aided analysis technology has made it possible to automatically detect lung nodules in Computed Tomography (CT) screening. In this paper, we propose a novel detection network, TiCNet. It is attempted to embed a transformer module in the 3D Convolutional Neural Network (CNN) for pulmonary nodule detection on CT images. First, we integrate the transformer and CNN in an end-to-end structure to capture both the short- and long-range dependency to provide rich information on the characteristics of nodules. Second, we design the attention block and multi-scale skip pathways for improving the detection of small nodules. Last, we develop a two-head detector to guarantee high sensitivity and specificity. Experimental results on the LUNA16 dataset and PN9 dataset showed that our proposed TiCNet achieved superior performance compared with existing lung nodule detection methods. Moreover, the effectiveness of each module has been proven. The proposed TiCNet model is an effective tool for pulmonary nodule detection. Validation revealed that this model exhibited excellent performance, suggesting its potential usefulness to support lung cancer screening | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a CT images | |
| 650 | 4 | |a Multi-scale | |
| 650 | 4 | |a Pulmonary nodule detection | |
| 650 | 4 | |a Transformer | |
| 700 | 1 | |a Li, Gen |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Xingyu |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Qiliang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Lizhi |e verfasserin |4 aut | |
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