The University of Chicago Header Logo

Connection

Co-Authors

This is a "connection" page, showing publications co-authored by Hiroyuki Abe and Junji Shiraishi.
Connection Strength

1.078
  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: 0.427
  2. 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: 0.085
  3. 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: 0.071
  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: 0.063
  5. Computer-aided diagnosis in chest radiology. Semin Ultrasound CT MR. 2004 Oct; 25(5):432-7.
    View in: PubMed
    Score: 0.062
  6. 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: 0.059
  7. 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: 0.059
  8. 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: 0.057
  9. 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: 0.055
  10. Evaluation of objective similarity measures for selecting similar images of mammographic lesions. J Digit Imaging. 2011 Feb; 24(1):75-85.
    View in: PubMed
    Score: 0.024
  11. 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: 0.022
  12. 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: 0.017
  13. Computer-aided diagnosis in thoracic CT. Semin Ultrasound CT MR. 2005 Oct; 26(5):357-63.
    View in: PubMed
    Score: 0.017
  14. False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad Radiol. 2005 Feb; 12(2):191-201.
    View in: PubMed
    Score: 0.016
  15. 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: 0.016
  16. Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys. 2003 Sep; 30(9):2440-54.
    View in: PubMed
    Score: 0.014
  17. [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: 0.014
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.