Co-Authors
This is a "connection" page, showing publications co-authored by Samuel G. Armato and Feng Li.
Connection Strength
2.849
-
Correlation of patient survival with clinical tumor measurements in malignant pleural mesothelioma. Eur Radiol. 2019 Jun; 29(6):2981-2988.
Score: 0.667
-
Convolutional Neural Networks for Segmentation of Malignant Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance). ArXiv. 2023 Nov 30.
Score: 0.234
-
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: 0.199
-
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: 0.180
-
Response. Chest. 2019 10; 156(4):810-811.
Score: 0.175
-
Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists. Chest. 2019 07; 156(1):112-119.
Score: 0.170
-
Letter to the Editor: Use of Publicly Available Image Resources. Acad Radiol. 2017 07; 24(7):916-917.
Score: 0.149
-
LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham). 2016 Oct; 3(4):044506.
Score: 0.145
-
Clinical significance of noncalcified lung nodules in patients with breast cancer. Breast Cancer Res Treat. 2016 Sep; 159(2):265-71.
Score: 0.141
-
LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. J Med Imaging (Bellingham). 2015 Apr; 2(2):020103.
Score: 0.128
-
Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 2015 Apr; 22(4):475-80.
Score: 0.127
-
Improved detection of focal pneumonia by chest radiography with bone suppression imaging. Eur Radiol. 2012 Dec; 22(12):2729-35.
Score: 0.106
-
Research imaging in an academic medical center. Acad Radiol. 2012 Jun; 19(6):762-71.
Score: 0.104
-
Dual energy subtraction and temporal subtraction chest radiography. J Thorac Imaging. 2008 May; 23(2):77-85.
Score: 0.080
-
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: 0.064
-
Automated lung nodule classification following automated nodule detection on CT: a serial approach. Med Phys. 2003 Jun; 30(6):1188-97.
Score: 0.057
-
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: 0.055
-
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: 0.055
-
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: 0.014