Samuel G. Armato to Sensitivity and Specificity
This is a "connection" page, showing publications Samuel G. Armato has written about Sensitivity and Specificity.
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Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis. J Digit Imaging. 2015 Dec; 28(6):704-17.
Score: 0.098
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Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of "truth". Acad Radiol. 2009 Jan; 16(1):28-38.
Score: 0.060
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The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans. Acad Radiol. 2007 Nov; 14(11):1409-21.
Score: 0.056
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Temporal subtraction in chest radiography: automated assessment of registration accuracy. Med Phys. 2006 May; 33(5):1239-49.
Score: 0.050
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Evaluation of semiautomated measurements of mesothelioma tumor thickness on CT scans. Acad Radiol. 2005 Oct; 12(10):1301-9.
Score: 0.048
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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: 0.046
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Automated lung segmentation for thoracic CT impact on computer-aided diagnosis. Acad Radiol. 2004 Sep; 11(9):1011-21.
Score: 0.045
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Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques. Med Phys. 2004 May; 31(5):1105-15.
Score: 0.044
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Image annotation for conveying automated lung nodule detection results to radiologists. Acad Radiol. 2003 Sep; 10(9):1000-7.
Score: 0.042
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Automated lung nodule classification following automated nodule detection on CT: a serial approach. Med Phys. 2003 Jun; 30(6):1188-97.
Score: 0.041
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Automated detection of lung nodules in CT scans: effect of image reconstruction algorithm. Med Phys. 2003 Mar; 30(3):461-72.
Score: 0.040
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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: 0.040
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Computer-aided diagnosis in medical imaging. IEEE Trans Med Imaging. 2001 Dec; 20(12):1205-8.
Score: 0.037
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Automated lung segmentation in digital lateral chest radiographs. Med Phys. 1998 Aug; 25(8):1507-20.
Score: 0.029
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Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules. J Am Coll Radiol. 2015 Apr; 12(4):390-5.
Score: 0.023
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Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 2015 Apr; 22(4):475-80.
Score: 0.023
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CT-based pulmonary artery measurements for the assessment of pulmonary hypertension. Acad Radiol. 2014 Apr; 21(4):523-30.
Score: 0.022
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Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions. Med Phys. 2013 Jun; 40(6):061906.
Score: 0.021
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Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Med Phys. 2008 Feb; 35(2):694-703.
Score: 0.014
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Evaluation of lung MDCT nodule annotation across radiologists and methods. Acad Radiol. 2006 Oct; 13(10):1254-65.
Score: 0.013
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Automated lung segmentation of diseased and artifact-corrupted magnetic resonance sections. Med Phys. 2006 Sep; 33(9):3085-93.
Score: 0.013
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Automated detection of lung nodules in CT scans: false-positive reduction with the radial-gradient index. Med Phys. 2006 Apr; 33(4):1133-40.
Score: 0.012
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Vessel tree reconstruction in thoracic CT scans with application to nodule detection. IEEE Trans Med Imaging. 2005 Apr; 24(4):486-99.
Score: 0.012
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Automated matching of temporally sequential CT sections. Med Phys. 2004 Dec; 31(12):3417-24.
Score: 0.011
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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: 0.010
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Digital chest radiography: effect of temporal subtraction images on detection accuracy. Radiology. 1997 Feb; 202(2):447-52.
Score: 0.007