Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans-Reference-Cited by-同舟云学术

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Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans

Published:2020-03-18 Issue:5 Volume:47 Page:2150-2160
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Farhangi M. Mehdi¹,Petrick Nicholas¹,Sahiner Berkman¹,Frigui Hichem²,Amini Amir A.³,Pezeshk Aria¹

Affiliation:

1. Division of Imaging, Diagnostics, and Software Reliability (DIDSR) OSEL, CDRH, FDA Silver Spring MD 20993USA

2. Multimedia Laboratory University of Louisville Louisville KY 40292USA

3. Medical Imaging Laboratory University of Louisville Louisville KY 40292USA

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14076

Reference37 articles.

1. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

2. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening

3. The National Lung Screening Trial: Overview and Study Design

4. Van GinnekenB SetioAA JacobsC CiompiF.Off‐the‐shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans inBiomedical Imaging (ISBI) 2015 IEEE 12th International Symposium on pages 286–289 IEEE 2015.

5. TangH KimDR XieX.Automated pulmonary nodule detection using 3D deep convolutional neural networks inBiomedical Imaging (ISBI 2018) 2018 IEEE 15th International Symposium on pages 523–526 IEEE 2018.

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