Robert Nishikawa to Reproducibility of Results
This is a "connection" page, showing publications Robert Nishikawa has written about Reproducibility of Results.
Connection Strength
1.100
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CADe for early detection of breast cancer-current status and why we need to continue to explore new approaches. Acad Radiol. 2014 Oct; 21(10):1320-1.
Score: 0.079
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Estimating sensitivity and specificity for technology assessment based on observer studies. Acad Radiol. 2013 Jul; 20(7):825-30.
Score: 0.072
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A comparison study of image features between FFDM and film mammogram images. Med Phys. 2012 Jul; 39(7):4386-94.
Score: 0.068
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Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med Phys. 2012 Feb; 39(2):676-85.
Score: 0.066
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On the orientation of mammographic structure. Med Phys. 2011 Oct; 38(10):5303-6.
Score: 0.065
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Detection of clustered microcalcifications using spatial point process modeling. Phys Med Biol. 2011 Jan 07; 56(1):1-17.
Score: 0.061
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Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. Med Phys. 2010 Apr; 37(4):1591-600.
Score: 0.058
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Comparison of power spectra for tomosynthesis projections and reconstructed images. Med Phys. 2009 May; 36(5):1753-8.
Score: 0.055
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Comparison of soft-copy and hard-copy reading for full-field digital mammography. Radiology. 2009 Apr; 251(1):41-9.
Score: 0.054
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Identification of simulated microcalcifications in white noise and mammographic backgrounds. Med Phys. 2006 Aug; 33(8):2905-11.
Score: 0.045
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Computer-aided detection, in its present form, is not an effective aid for screening mammography. For the proposition. Med Phys. 2006 Apr; 33(4):811-2.
Score: 0.044
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The hypervolume under the ROC hypersurface of "near-guessing" and "near-perfect" observers in N-class classification tasks. IEEE Trans Med Imaging. 2005 Mar; 24(3):293-9.
Score: 0.041
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Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance. Med Phys. 2004 Sep; 31(9):2648-57.
Score: 0.040
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Estimating three-class ideal observer decision variables for computerized detection and classification of mammographic mass lesions. Med Phys. 2004 Jan; 31(1):81-90.
Score: 0.038
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The use of a priori information in the detection of mammographic microcalcifications to improve their classification. Med Phys. 2003 May; 30(5):823-31.
Score: 0.036
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A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002 Dec; 21(12):1552-63.
Score: 0.035
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Computer-aided diagnosis complements full-field digital mammography. Diagn Imaging (San Franc). 1999 Sep; 21(9):47-51, 75.
Score: 0.028
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Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications. IEEE Trans Med Imaging. 2017 05; 36(5):1162-1171.
Score: 0.023
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Comparison of eye position versus computer identified microcalcification clusters on mammograms. Med Phys. 1997 Jan; 24(1):17-23.
Score: 0.023
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Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer. Breast Cancer Res. 2016 07 22; 18(1):76.
Score: 0.023
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Using breast radiographers' reports as a second opinion for radiologists' readings of microcalcifications in digital mammography. Br J Radiol. 2015 Mar; 88(1047):20140565.
Score: 0.020
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Analysis of perceived similarity between pairs of microcalcification clusters in mammograms. Med Phys. 2014 May; 41(5):051904.
Score: 0.019
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Stereoscopic digital mammography: improved specificity and reduced rate of recall in a prospective clinical trial. Radiology. 2013 Jan; 266(1):81-8.
Score: 0.017
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Automated detection of mass lesions in dedicated breast CT: a preliminary study. Med Phys. 2012 Feb; 39(2):866-73.
Score: 0.017
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Contrast enhancement of hepatic hemangiomas on multiphase MDCT: Can we diagnose hepatic hemangiomas by comparing enhancement with blood pool? AJR Am J Roentgenol. 2010 Aug; 195(2):381-6.
Score: 0.015
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Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study. Med Phys. 2008 Apr; 35(4):1486-93.
Score: 0.013
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Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Trans Med Imaging. 2005 Oct; 24(10):1278-85.
Score: 0.011
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A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans Med Imaging. 2005 Mar; 24(3):371-80.
Score: 0.010
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A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004 Oct; 23(10):1233-44.
Score: 0.010
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Investigation of physical image quality indices of a bone densitometry system. Med Phys. 2004 Apr; 31(4):873-81.
Score: 0.010
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Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms. Med Phys. 1998 Aug; 25(8):1502-6.
Score: 0.006