The University of Chicago Header Logo

Connection

Samuel Armato to Solitary Pulmonary Nodule

This is a "connection" page, showing publications Samuel Armato has written about Solitary Pulmonary Nodule.
Connection Strength

2.596
  1. 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.
    View in: PubMed
    Score: 0.429
  2. Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of "truth". Acad Radiol. 2009 Jan; 16(1):28-38.
    View in: PubMed
    Score: 0.266
  3. 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.
    View in: PubMed
    Score: 0.245
  4. 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.
    View in: PubMed
    Score: 0.204
  5. Image annotation for conveying automated lung nodule detection results to radiologists. Acad Radiol. 2003 Sep; 10(9):1000-7.
    View in: PubMed
    Score: 0.183
  6. Automated lung nodule classification following automated nodule detection on CT: a serial approach. Med Phys. 2003 Jun; 30(6):1188-97.
    View in: PubMed
    Score: 0.180
  7. Automated detection of lung nodules in CT scans: effect of image reconstruction algorithm. Med Phys. 2003 Mar; 30(3):461-72.
    View in: PubMed
    Score: 0.177
  8. Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology. 2002 Dec; 225(3):685-92.
    View in: PubMed
    Score: 0.174
  9. Computerized detection of pulmonary nodules on CT scans. Radiographics. 1999 Sep-Oct; 19(5):1303-11.
    View in: PubMed
    Score: 0.139
  10. 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.
    View in: PubMed
    Score: 0.102
  11. Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 2015 Apr; 22(4):475-80.
    View in: PubMed
    Score: 0.101
  12. The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined "truth". Acad Radiol. 2007 Dec; 14(12):1455-63.
    View in: PubMed
    Score: 0.062
  13. The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements. Acad Radiol. 2007 Dec; 14(12):1475-85.
    View in: PubMed
    Score: 0.062
  14. Evaluation of lung MDCT nodule annotation across radiologists and methods. Acad Radiol. 2006 Oct; 13(10):1254-65.
    View in: PubMed
    Score: 0.057
  15. Automated detection of lung nodules in CT scans: false-positive reduction with the radial-gradient index. Med Phys. 2006 Apr; 33(4):1133-40.
    View in: PubMed
    Score: 0.055
  16. Vessel tree reconstruction in thoracic CT scans with application to nodule detection. IEEE Trans Med Imaging. 2005 Apr; 24(4):486-99.
    View in: PubMed
    Score: 0.051
  17. Automated lung segmentation for thoracic CT impact on computer-aided diagnosis. Acad Radiol. 2004 Sep; 11(9):1011-21.
    View in: PubMed
    Score: 0.049
  18. 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.
    View in: PubMed
    Score: 0.045
  19. The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Acad Radiol. 2007 Dec; 14(12):1464-74.
    View in: PubMed
    Score: 0.015
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.