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Connection

Hiroyuki Abe to ROC Curve

This is a "connection" page, showing publications Hiroyuki Abe has written about ROC Curve.
Connection Strength

0.922
  1. Basic concepts and development of an all-purpose computer interface for ROC/FROC observer study. Radiol Phys Technol. 2013 Jan; 6(1):35-41.
    View in: PubMed
    Score: 0.344
  2. Diffusion weighted images of metastatic as compared with nonmetastatic axillary lymph nodes in patients with newly diagnosed breast cancer. J Magn Reson Imaging. 2015 Sep; 42(3):771-8.
    View in: PubMed
    Score: 0.102
  3. Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients. Int J Comput Assist Radiol Surg. 2013 Nov; 8(6):895-903.
    View in: PubMed
    Score: 0.090
  4. Effect of temporal subtraction images on radiologists' detection of lung cancer on CT: results of the observer performance study with use of film computed tomography images. Acad Radiol. 2004 Dec; 11(12):1337-43.
    View in: PubMed
    Score: 0.051
  5. Artificial neural networks (ANNs) for differential diagnosis of interstitial lung disease: results of a simulation test with actual clinical cases. Acad Radiol. 2004 Jan; 11(1):29-37.
    View in: PubMed
    Score: 0.048
  6. 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: 0.044
  7. Use of an artificial neural network to determine the diagnostic value of specific clinical and radiologic parameters in the diagnosis of interstitial lung disease on chest radiographs. Acad Radiol. 2002 Jan; 9(1):13-7.
    View in: PubMed
    Score: 0.041
  8. Evaluation of Kinetic Entropy of Breast Masses Initially Found on MRI using Whole-lesion Curve Distribution Data: Comparison with the Standard Kinetic Analysis. Eur Radiol. 2015 Aug; 25(8):2470-8.
    View in: PubMed
    Score: 0.026
  9. Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era. Eur J Radiol. 2015 Mar; 84(3):392-397.
    View in: PubMed
    Score: 0.025
  10. Observer study of a prototype clinical decision support system for breast cancer diagnosis using dynamic contrast-enhanced MRI. AJR Am J Roentgenol. 2013 Feb; 200(2):277-83.
    View in: PubMed
    Score: 0.022
  11. Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study. Radiology. 2011 Mar; 258(3):696-704.
    View in: PubMed
    Score: 0.019
  12. Potential usefulness of similar images in the differential diagnosis of clustered microcalcifications on mammograms. Radiology. 2009 Dec; 253(3):625-31.
    View in: PubMed
    Score: 0.018
  13. Observer study for evaluating potential utility of a super-high-resolution LCD in the detection of clustered microcalcifications on digital mammograms. J Digit Imaging. 2010 Apr; 23(2):161-9.
    View in: PubMed
    Score: 0.017
  14. 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: 0.014
  15. Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology. 2005 Nov; 237(2):684-90.
    View in: PubMed
    Score: 0.014
  16. 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: 0.013
  17. Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys. 2003 Sep; 30(9):2440-54.
    View in: PubMed
    Score: 0.012
  18. Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience. Radiology. 2003 May; 227(2):469-74.
    View in: PubMed
    Score: 0.011
  19. [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: 0.011
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.