Robert Nishikawa to Sensitivity and Specificity
This is a "connection" page, showing publications Robert Nishikawa has written about Sensitivity and Specificity.
Connection Strength
1.324
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Locally adaptive decision in detection of clustered microcalcifications in mammograms. Phys Med Biol. 2018 02 15; 63(4):045014.
Score: 0.109
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Importance of Better Human-Computer Interaction in the Era of Deep Learning: Mammography Computer-Aided Diagnosis as a Use Case. J Am Coll Radiol. 2018 01; 15(1 Pt A):49-52.
Score: 0.107
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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: 0.085
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Estimating sensitivity and specificity for technology assessment based on observer studies. Acad Radiol. 2013 Jul; 20(7):825-30.
Score: 0.078
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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: 0.074
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Clinically missed cancer: how effectively can radiologists use computer-aided detection? AJR Am J Roentgenol. 2012 Mar; 198(3):708-16.
Score: 0.072
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Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med Phys. 2012 Feb; 39(2):676-85.
Score: 0.072
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Detection of clustered microcalcifications using spatial point process modeling. Phys Med Biol. 2011 Jan 07; 56(1):1-17.
Score: 0.066
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Comparison of power spectra for tomosynthesis projections and reconstructed images. Med Phys. 2009 May; 36(5):1753-8.
Score: 0.059
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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: 0.059
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Identification of simulated microcalcifications in white noise and mammographic backgrounds. Med Phys. 2006 Aug; 33(8):2905-11.
Score: 0.049
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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: 0.048
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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: 0.043
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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: 0.039
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A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002 Dec; 21(12):1552-63.
Score: 0.038
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Computer-aided diagnosis complements full-field digital mammography. Diagn Imaging (San Franc). 1999 Sep; 21(9):47-51, 75.
Score: 0.030
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Optimization and FROC analysis of rule-based detection schemes using a multiobjective approach. IEEE Trans Med Imaging. 1998 Dec; 17(6):1089-93.
Score: 0.029
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Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography. Med Phys. 2017 Jul; 44(7):3726-3738.
Score: 0.026
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A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology. 1996 Dec; 201(3):745-50.
Score: 0.025
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Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model. Med Phys. 2016 Jan; 43(1):159.
Score: 0.023
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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: 0.019
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Assessing the stand-alone sensitivity of computer-aided detection with cancer cases from the Digital Mammographic Imaging Screening Trial. AJR Am J Roentgenol. 2012 Sep; 199(3):W392-401.
Score: 0.019
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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: 0.018
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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: 0.016
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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: 0.014
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Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications. Acad Radiol. 2006 Jan; 13(1):84-94.
Score: 0.012
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Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Trans Med Imaging. 2005 Oct; 24(10):1278-85.
Score: 0.012
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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: 0.011
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Computerized detection of mass lesions in digital breast tomosynthesis images using two- and three dimensional radial gradient index segmentation. Technol Cancer Res Treat. 2004 Oct; 3(5):437-41.
Score: 0.011
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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: 0.011
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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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Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications. Radiology. 2001 Sep; 220(3):787-94.
Score: 0.009
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Radiologists' preferences for digital mammographic display. The International Digital Mammography Development Group. Radiology. 2000 Sep; 216(3):820-30.
Score: 0.008
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Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol. 1999 Jan; 6(1):22-33.
Score: 0.007
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A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms. Med Phys. 1998 Sep; 25(9):1613-20.
Score: 0.007
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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: 0.007
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Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology. 1996 Mar; 198(3):671-8.
Score: 0.006