Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules-Reference-Cited by-同舟云学术

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Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules

Published:2023-08-09 Issue:04 Volume:10 Page:
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Selby Heather M.¹,Mukherjee Pritam²,Parham Christopher³,Malik Sachin B.³,Gevaert Olivier¹,Napel Sandy⁴,Shah Rajesh P.³

Affiliation:

1. Stanford University School of Medicine, Stanford Center for Biomedical Informatics (BMIR), Stanford, California, United States

2. National Institutes of Health Clinical Center, Bethesda, Maryland, United States

3. Veterans Affairs Palo Alto Health Care System, Palo Alto, California, United States

4. Stanford University School of Medicine, Department of Radiology, Stanford, California, United States

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Reference39 articles.

1. The Potential of Radiomic-Based Phenotyping in Precision Medicine

2. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

3. Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography

4. Predictive radiogenomics modeling of EGFR mutation status in lung cancer

5. Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results

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