Comparison of deep learning and human observer performance for detection and characterization of simulated lesions-Reference-Cited by-同舟云学术

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Comparison of deep learning and human observer performance for detection and characterization of simulated lesions

Published:2019-06-21 Issue:02 Volume:6 Page:1
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

De Man Ruben¹,Gang Grace J.²,Li Xin³,Wang Ge⁴

Affiliation:

1. Stony Brook University, Department of Biochemistry and Cell Biology, Stony Brook, New York

2. Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland

3. GE Global Research, Radiation Imaging Sciences, Niskayuna, New York

4. Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Reference22 articles.

1. Survival of Patients with Stage I Lung Cancer Detected on CT Screening

2. Tumour markers: An overview

4. Cascaded systems analysis of noise reduction algorithms in dual-energy imaging

5. Upper-Bound on Dose Reduction in CT Reconstruction for Nodule Detection

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