 Connection
Co-Authors
This is a "connection" page, showing publications co-authored by Maryellen L. Giger and Feng Li.
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Connection Strength |
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0.859 |
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Li F, Armato SG, Engelmann R, Rhines T, Crosby J, Lan L, Giger ML, MacMahon H. Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs. J Digit Imaging. 2021 08; 34(4):922-931.
Score: 0.230
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Crosby J, Rhines T, Li F, MacMahon H, Giger M. Deep convolutional neural networks in the classification of dual-energy thoracic radiographic views for efficient workflow: analysis on over 6500 clinical radiographs. J Med Imaging (Bellingham). 2020 Jan; 7(1):016501.
Score: 0.208
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Li F, Armato SG, Giger ML, MacMahon H. Clinical significance of noncalcified lung nodules in patients with breast cancer. Breast Cancer Res Treat. 2016 Sep; 159(2):265-71.
Score: 0.163
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Armato SG, Gruszauskas NP, Macmahon H, Torno MD, Li F, Engelmann RM, Starkey A, Pudela CL, Marino JS, Santiago F, Chang PJ, Giger ML. Research imaging in an academic medical center. Acad Radiol. 2012 Jun; 19(6):762-71.
Score: 0.121
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Armato SG, Drukker K, Li F, Hadjiiski L, Tourassi GD, Engelmann RM, Giger ML, Redmond G, Farahani K, Kirby JS, Petrick NA. Letter to the Editor: Use of Publicly Available Image Resources. Acad Radiol. 2017 07; 24(7):916-917.
Score: 0.043
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Armato SG, Drukker K, Li F, Hadjiiski L, Tourassi GD, Engelmann RM, Giger ML, Redmond G, Farahani K, Kirby JS, Clarke LP. LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham). 2016 Oct; 3(4):044506.
Score: 0.042
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Armato SG, Hadjiiski L, Tourassi GD, Drukker K, Giger ML, Li F, Redmond G, Farahani K, Kirby JS, Clarke LP. LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. J Med Imaging (Bellingham). 2015 Apr; 2(2):020103.
Score: 0.037
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Armato SG, Li F, Giger ML, MacMahon H, Sone S, Doi K. Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology. 2002 Dec; 225(3):685-92.
Score: 0.016
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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