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Connection

Kunio Doi to Solitary Pulmonary Nodule

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

6.081
  1. Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: receiver operating characteristic analysis with and without localization. AJR Am J Roentgenol. 2011 May; 196(5):W535-41.
    View in: PubMed
    Score: 0.362
  2. Usefulness of computer-aided diagnosis schemes for vertebral fractures and lung nodules on chest radiographs. AJR Am J Roentgenol. 2008 Jul; 191(1):260-5.
    View in: PubMed
    Score: 0.298
  3. An investigation of radiologists' perception of lesion similarity: observations with paired breast masses on mammograms and paired lung nodules on CT images. Acad Radiol. 2008 Jul; 15(7):887-94.
    View in: PubMed
    Score: 0.298
  4. Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad Radiol. 2008 Feb; 15(2):165-75.
    View in: PubMed
    Score: 0.289
  5. Improving radiologists' recommendations with computer-aided diagnosis for management of small nodules detected by CT. Acad Radiol. 2006 Aug; 13(8):943-50.
    View in: PubMed
    Score: 0.261
  6. Integrating PET and CT information to improve diagnostic accuracy for lung nodules: A semiautomatic computer-aided method. J Nucl Med. 2006 Jul; 47(7):1075-80.
    View in: PubMed
    Score: 0.259
  7. [Comparison of LCD and CRT monitors for detection of pulmonary nodules and interstitial lung diseases on digital chest radiographs by using receiver operating characteristic analysis]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2006 May 20; 62(5):734-41.
    View in: PubMed
    Score: 0.257
  8. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging. 2006 Apr; 25(4):406-16.
    View in: PubMed
    Score: 0.255
  9. How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? Acad Radiol. 2005 Oct; 12(10):1333-41.
    View in: PubMed
    Score: 0.246
  10. Computer-aided diagnosis in thoracic CT. Semin Ultrasound CT MR. 2005 Oct; 26(5):357-63.
    View in: PubMed
    Score: 0.246
  11. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans Med Imaging. 2005 Sep; 24(9):1138-50.
    View in: PubMed
    Score: 0.245
  12. [Report on the 90th Scientific Assembly and Annual Meeting of the Radiological Society of North America-- Comparison of LCD and CRT monitors for detection of pulmonary nodules and interstitial lung diseases on digital chest radiographs by using receiver operating characteristic analysis]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2005 Jul 20; 61(7):974-5.
    View in: PubMed
    Score: 0.243
  13. Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. AJR Am J Roentgenol. 2004 Nov; 183(5):1209-15.
    View in: PubMed
    Score: 0.231
  14. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR Am J Roentgenol. 2004 Feb; 182(2):505-10.
    View in: PubMed
    Score: 0.219
  15. Effect of high sensitivity in a computerized scheme for detecting extremely subtle solitary pulmonary nodules in chest radiographs: observer performance study. Acad Radiol. 2003 Nov; 10(11):1302-11.
    View in: PubMed
    Score: 0.215
  16. 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: 0.210
  17. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. Med Phys. 2003 Mar; 30(3):387-94.
    View in: PubMed
    Score: 0.206
  18. 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: 0.202
  19. Detection of lung nodules on digital chest radiographs: potential usefulness of a new contralateral subtraction technique. Radiology. 2002 Apr; 223(1):199-203.
    View in: PubMed
    Score: 0.193
  20. Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. AJR Am J Roentgenol. 2002 Mar; 178(3):657-63.
    View in: PubMed
    Score: 0.192
  21. Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. Radiology. 2000 Mar; 214(3):823-30.
    View in: PubMed
    Score: 0.167
  22. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. AJR Am J Roentgenol. 2000 Jan; 174(1):71-4.
    View in: PubMed
    Score: 0.165
  23. Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test. Radiology. 1999 Dec; 213(3):723-6.
    View in: PubMed
    Score: 0.164
  24. A computerized scheme for lung nodule detection in multiprojection chest radiography. Med Phys. 2012 Apr; 39(4):2001-12.
    View in: PubMed
    Score: 0.096
  25. Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs ROC analysis of radiologists' performance. Acad Radiol. 2006 Aug; 13(8):995-1003.
    View in: PubMed
    Score: 0.065
  26. 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: 0.059
  27. Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol. 2004 Jun; 11(6):617-29.
    View in: PubMed
    Score: 0.056
  28. 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: 0.052
  29. Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies. Radiographics. 2003 Jan-Feb; 23(1):255-65.
    View in: PubMed
    Score: 0.051
  30. Independent versus sequential reading in ROC studies of computer-assist modalities: analysis of components of variance. Acad Radiol. 2002 Sep; 9(9):1036-43.
    View in: PubMed
    Score: 0.050
  31. [Development of an image processing scheme for chest radiographs using a dot printer]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2002 Sep; 58(9):1268-77.
    View in: PubMed
    Score: 0.050
  32. Computerized detection of pulmonary nodules on CT scans. Radiographics. 1999 Sep-Oct; 19(5):1303-11.
    View in: PubMed
    Score: 0.040
  33. Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol. 1999 Aug; 31(2):97-109.
    View in: PubMed
    Score: 0.040
  34. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme. J Digit Imaging. 1994 Nov; 7(4):196-207.
    View in: PubMed
    Score: 0.029
  35. Potential usefulness of computerized nodule detection in screening programs for lung cancer. Invest Radiol. 1992 Jun; 27(6):471-5.
    View in: PubMed
    Score: 0.024
  36. Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections. Med Phys. 1990 Sep-Oct; 17(5):861-5.
    View in: PubMed
    Score: 0.022
  37. Pulmonary nodules: computer-aided detection in digital chest images. Radiographics. 1990 Jan; 10(1):41-51.
    View in: PubMed
    Score: 0.021
  38. Data compression: effect on diagnostic accuracy in digital chest radiography. Radiology. 1991 Jan; 178(1):175-9.
    View in: PubMed
    Score: 0.006
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.