3D deep learning for detecting pulmonary nodules in CT scans.
Objective: To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules.Materials and Methods: The proposed system uses 2 3D deep learning models, 1 for each of the essential tasks of computer-aided nodule detection: candidate generation...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 25; no. 10; pp. 1301 - 1311 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Oct2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132389131&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132389131 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Oct2018 vid: 25 iid: 10 pid: 622 pub: Oxford University Press / USA artinfo: ui: 132389131 132389131 NLM30137371 132389131 10.1093/jamia/ocy098 NLM30137371 132389131 ppf: 1301 ppct: 10 formats: tig: atl: 3D deep learning for detecting pulmonary nodules in CT scans. aug: au: Gruetzemacher, Ross Gupta, Ashish Paradice, David affil: Department of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA 36849 sug: subj: Solitary Pulmonary Nodule Diagnosis, Computer Assisted Lung Neoplasms Tomography, X-Ray Computed Lung Human Sensitivity and Specificity ROC Curve Radiographic Image Interpretation, Computer-Assisted Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Objective: To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules.Materials and Methods: The proposed system uses 2 3D deep learning models, 1 for each of the essential tasks of computer-aided nodule detection: candidate generation and false positive reduction. A total of 888 scans from the LIDC-IDRI dataset were used for training and evaluation.Results: Results for candidate generation on the test data indicated a detection rate of 94.77% with 30.39 false positives per scan, while the test results for false positive reduction exhibited a sensitivity of 94.21% with 1.789 false positives per scan. The overall system detection rate on the test data was 89.29% with 1.789 false positives per scan.Discussion: An extensive and rigorous validation was conducted to assess the performance of the proposed system. The system demonstrated a novel combination of 3D deep neural network architectures and demonstrates the use of deep learning for both candidate generation and false positive reduction to be evaluated with a substantial test dataset. The results strongly support the ability of deep learning pulmonary nodule detection systems to generalize to unseen data. The source code and trained model weights have been made available.Conclusion: A novel deep-neural-network-based pulmonary nodule detection system is demonstrated and validated. The results provide comparison of the proposed deep-learning-based system over other similar systems based on performance. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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