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Connection

Kunio Doi to Artificial Intelligence

This is a "connection" page, showing publications Kunio Doi has written about Artificial Intelligence.

 
Connection Strength
 
 
 
2.115
 
  1. Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin Nucl Med. 2011 Nov; 41(6):449-62.
    View in: PubMed
    Score: 0.386
  2. Sugimoto K, Shiraishi J, Moriyasu F, Doi K. Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography. Acad Radiol. 2009 Apr; 16(4):401-11.
    View in: PubMed
    Score: 0.322
  3. Muramatsu C, Li Q, Schmidt RA, Shiraishi J, Doi K. Determination of similarity measures for pairs of mass lesions on mammograms by use of BI-RADS lesion descriptors and image features. Acad Radiol. 2009 Apr; 16(4):443-9.
    View in: PubMed
    Score: 0.322
  4. Suzuki K, Abe H, MacMahon H, Doi K. 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: 0.262
  5. Muramatsu C, Li Q, Suzuki K, Schmidt RA, Shiraishi J, Newstead GM, Doi K. Investigation of psychophysical measure for evaluation of similar images for mammographic masses: preliminary results. Med Phys. 2005 Jul; 32(7):2295-304.
    View in: PubMed
    Score: 0.249
  6. Wang J, Li F, Doi K, Li Q. Computerized detection of diffuse lung disease in MDCT: the usefulness of statistical texture features. Phys Med Biol. 2009 Nov 21; 54(22):6881-99.
    View in: PubMed
    Score: 0.084
  7. Li F, Kumazawa S, Shiraishi J, Li Q, Engelmann R, Caligiuri P, MacMahon H, Doi K. Subjective similarity of patterns of diffuse interstitial lung disease on thin-section CT: an observer performance study. Acad Radiol. 2009 Apr; 16(4):477-85.
    View in: PubMed
    Score: 0.081
  8. Kasai S, Li F, Shiraishi J, Doi K. 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: 0.077
  9. Shiraishi J, Li Q, Appelbaum D, Pu Y, Doi K. Development of a computer-aided diagnostic scheme for detection of interval changes in successive whole-body bone scans. Med Phys. 2007 Jan; 34(1):25-36.
    View in: PubMed
    Score: 0.069
  10. Nakayama R, Watanabe R, Namba K, Takeda K, Yamamoto K, Katsuragawa S, Doi K. Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms. Acad Radiol. 2006 Oct; 13(10):1219-28.
    View in: PubMed
    Score: 0.068
  11. Suzuki K, Doi K. 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: 0.063
  12. Suzuki K, Li F, Sone S, Doi K. 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: 0.063
  13. Nakayama R, Uchiyama Y, Watanabe R, Katsuragawa S, Namba K, Doi K. Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. Med Phys. 2004 Apr; 31(4):789-99.
    View in: PubMed
    Score: 0.057
  14. van Ginneken B, Katsuragawa S, ter Haar Romeny BM, Doi K, Viergever MA. Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Trans Med Imaging. 2002 Feb; 21(2):139-49.
    View in: PubMed
    Score: 0.012
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.