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Connection

Samuel G. Armato to Reproducibility of Results

This is a "connection" page, showing publications Samuel G. Armato has written about Reproducibility of Results.
Connection Strength

0.892
  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: 0.086
  2. Comparison of Two Deformable Registration Algorithms in the Presence of Radiologic Change Between Serial Lung CT Scans. J Digit Imaging. 2015 Dec; 28(6):755-60.
    View in: PubMed
    Score: 0.086
  3. Lung texture in serial thoracic CT scans: assessment of change introduced by image registration. Med Phys. 2012 Aug; 39(8):4679-90.
    View in: PubMed
    Score: 0.068
  4. 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: 0.053
  5. 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: 0.049
  6. Temporal subtraction in chest radiography: automated assessment of registration accuracy. Med Phys. 2006 May; 33(5):1239-49.
    View in: PubMed
    Score: 0.044
  7. Evaluation of semiautomated measurements of mesothelioma tumor thickness on CT scans. Acad Radiol. 2005 Oct; 12(10):1301-9.
    View in: PubMed
    Score: 0.043
  8. Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques. Med Phys. 2004 May; 31(5):1105-15.
    View in: PubMed
    Score: 0.039
  9. 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: 0.036
  10. 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: 0.036
  11. AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging. Med Phys. 2023 Feb; 50(2):e1-e24.
    View in: PubMed
    Score: 0.035
  12. Computer-aided diagnosis in medical imaging. IEEE Trans Med Imaging. 2001 Dec; 20(12):1205-8.
    View in: PubMed
    Score: 0.033
  13. Automated detection of lung nodules in CT scans: preliminary results. Med Phys. 2001 Aug; 28(8):1552-61.
    View in: PubMed
    Score: 0.032
  14. 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.
    View in: PubMed
    Score: 0.032
  15. Computer-assisted Curie scoring for metaiodobenzylguanidine (MIBG) scans in patients with neuroblastoma. Pediatr Blood Cancer. 2018 12; 65(12):e27417.
    View in: PubMed
    Score: 0.026
  16. Automated lung segmentation in digital lateral chest radiographs. Med Phys. 1998 Aug; 25(8):1507-20.
    View in: PubMed
    Score: 0.026
  17. 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: 0.021
  18. 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: 0.020
  19. CT-based pulmonary artery measurements for the assessment of pulmonary hypertension. Acad Radiol. 2014 Apr; 21(4):523-30.
    View in: PubMed
    Score: 0.019
  20. Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions. Med Phys. 2013 Jun; 40(6):061906.
    View in: PubMed
    Score: 0.018
  21. Computerized segmentation and measurement of malignant pleural mesothelioma. Med Phys. 2011 Jan; 38(1):238-44.
    View in: PubMed
    Score: 0.015
  22. 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.
    View in: PubMed
    Score: 0.012
  23. Evaluation of lung MDCT nodule annotation across radiologists and methods. Acad Radiol. 2006 Oct; 13(10):1254-65.
    View in: PubMed
    Score: 0.011
  24. Automated lung segmentation of diseased and artifact-corrupted magnetic resonance sections. Med Phys. 2006 Sep; 33(9):3085-93.
    View in: PubMed
    Score: 0.011
  25. 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: 0.011
  26. 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: 0.010
  27. Automated matching of temporally sequential CT sections. Med Phys. 2004 Dec; 31(12):3417-24.
    View in: PubMed
    Score: 0.010
  28. 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: 0.009
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.