 Connection
Co-Authors
This is a "connection" page, showing publications co-authored by Samuel G. Armato and Feng Li.
|
|
| |
Connection Strength |
|
 |
|
 |
| |
2.954 |
|
|
|
-
Li F, Ahmad M, Qayyum F, Straus CM, MacMahon H, Kindler H, Armato SG. Correlation of patient survival with clinical tumor measurements in malignant pleural mesothelioma. Eur Radiol. 2019 Jun; 29(6):2981-2988.
Score: 0.754
-
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: 0.225
-
Gudmundsson E, Straus CM, Li F, Armato SG. Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion. J Med Imaging (Bellingham). 2020 Jan; 7(1):012705.
Score: 0.203
-
MacMahon H, Li F, Jiang Y, Armato SG. Response. Chest. 2019 10; 156(4):810-811.
Score: 0.198
-
MacMahon H, Li F, Jiang Y, Armato SG. Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That?of Radiologists. Chest. 2019 07; 156(1):112-119.
Score: 0.192
-
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: 0.168
-
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: 0.163
-
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: 0.159
-
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: 0.145
-
Li F, Engelmann R, Armato SG, MacMahon H. Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 2015 Apr; 22(4):475-80.
Score: 0.143
-
Li F, Engelmann R, Pesce L, Armato SG, Macmahon H. Improved detection of focal pneumonia by chest radiography with bone suppression imaging. Eur Radiol. 2012 Dec; 22(12):2729-35.
Score: 0.120
-
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: 0.118
-
MacMahon H, Li F, Engelmann R, Roberts R, Armato S. Dual energy subtraction and temporal subtraction chest radiography. J Thorac Imaging. 2008 May; 23(2):77-85.
Score: 0.090
-
Armato SG, Roy AS, Macmahon H, Li F, Doi K, Sone S, Altman MB. Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program(1). Acad Radiol. 2005 Mar; 12(3):337-46.
Score: 0.072
-
Armato SG, Altman MB, Wilkie J, Sone S, Li F, Doi K, Roy AS. Automated lung nodule classification following automated nodule detection on CT: a serial approach. Med Phys. 2003 Jun; 30(6):1188-97.
Score: 0.064
-
Li F, Sone S, Abe H, MacMahon H, Armato SG, Doi K. Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. Radiology. 2002 Dec; 225(3):673-83.
Score: 0.062
-
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: 0.062
-
Suzuki K, Armato SG, Li F, Sone S, Doi K. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys. 2003 Jul; 30(7):1602-17.
Score: 0.016
|
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.
|