Kunio Doi to Artificial Intelligence
This is a "connection" page, showing publications Kunio Doi has written about Artificial Intelligence.
Connection Strength
1.824
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Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin Nucl Med. 2011 Nov; 41(6):449-62.
Score: 0.333
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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.
Score: 0.278
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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.
Score: 0.278
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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.
Score: 0.226
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Investigation of psychophysical measure for evaluation of similar images for mammographic masses: preliminary results. Med Phys. 2005 Jul; 32(7):2295-304.
Score: 0.215
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Computerized detection of diffuse lung disease in MDCT: the usefulness of statistical texture features. Phys Med Biol. 2009 Nov 21; 54(22):6881-99.
Score: 0.072
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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.
Score: 0.070
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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.
Score: 0.066
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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.
Score: 0.060
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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.
Score: 0.058
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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.
Score: 0.055
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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.
Score: 0.054
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Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. Med Phys. 2004 Apr; 31(4):789-99.
Score: 0.049
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Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Trans Med Imaging. 2002 Feb; 21(2):139-49.
Score: 0.011