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Connection

Robert Nishikawa to Mammography

This is a "connection" page, showing publications Robert Nishikawa has written about Mammography.
Connection Strength

10.590
  1. Breast Cancer Screening Interval: Effect on Rate of Late-Stage Disease at Diagnosis and Overall Survival. J Clin Oncol. 2024 Nov 10; 42(32):3837-3846.
    View in: PubMed
    Score: 0.748
  2. Locally adaptive decision in detection of clustered microcalcifications in mammograms. Phys Med Biol. 2018 02 15; 63(4):045014.
    View in: PubMed
    Score: 0.476
  3. 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.
    View in: PubMed
    Score: 0.467
  4. 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.
    View in: PubMed
    Score: 0.373
  5. Estimating sensitivity and specificity for technology assessment based on observer studies. Acad Radiol. 2013 Jul; 20(7):825-30.
    View in: PubMed
    Score: 0.342
  6. Point/counterpoint: computer-aided detection should be used routinely to assist screening mammogram interpretation. Med Phys. 2012 Sep; 39(9):5305-7.
    View in: PubMed
    Score: 0.326
  7. A comparison study of image features between FFDM and film mammogram images. Med Phys. 2012 Jul; 39(7):4386-94.
    View in: PubMed
    Score: 0.323
  8. Clinically missed cancer: how effectively can radiologists use computer-aided detection? AJR Am J Roentgenol. 2012 Mar; 198(3):708-16.
    View in: PubMed
    Score: 0.315
  9. Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med Phys. 2012 Feb; 39(2):676-85.
    View in: PubMed
    Score: 0.313
  10. Re: effectiveness of computer-aided detection in community mammography practice. J Natl Cancer Inst. 2012 Jan 04; 104(1):77; author reply 78-9.
    View in: PubMed
    Score: 0.311
  11. On the orientation of mammographic structure. Med Phys. 2011 Oct; 38(10):5303-6.
    View in: PubMed
    Score: 0.306
  12. Detection of clustered microcalcifications using spatial point process modeling. Phys Med Biol. 2011 Jan 07; 56(1):1-17.
    View in: PubMed
    Score: 0.289
  13. Comparison of power spectra for tomosynthesis projections and reconstructed images. Med Phys. 2009 May; 36(5):1753-8.
    View in: PubMed
    Score: 0.259
  14. Comparison of soft-copy and hard-copy reading for full-field digital mammography. Radiology. 2009 Apr; 251(1):41-9.
    View in: PubMed
    Score: 0.258
  15. Computer-aided screening mammography. N Engl J Med. 2007 Jul 05; 357(1):84; author reply 85.
    View in: PubMed
    Score: 0.228
  16. Current status and future directions of computer-aided diagnosis in mammography. Comput Med Imaging Graph. 2007 Jun-Jul; 31(4-5):224-35.
    View in: PubMed
    Score: 0.224
  17. Identification of simulated microcalcifications in white noise and mammographic backgrounds. Med Phys. 2006 Aug; 33(8):2905-11.
    View in: PubMed
    Score: 0.214
  18. 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.
    View in: PubMed
    Score: 0.209
  19. Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance. Med Phys. 2004 Sep; 31(9):2648-57.
    View in: PubMed
    Score: 0.187
  20. Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover. Breast Cancer Res. 2024 02 01; 26(1):21.
    View in: PubMed
    Score: 0.180
  21. Estimating three-class ideal observer decision variables for computerized detection and classification of mammographic mass lesions. Med Phys. 2004 Jan; 31(1):81-90.
    View in: PubMed
    Score: 0.179
  22. AI in Screening Mammography: Use One Radiologist and Improve Double Reads. Radiology. 2023 11; 309(2):e232964.
    View in: PubMed
    Score: 0.177
  23. The use of a priori information in the detection of mammographic microcalcifications to improve their classification. Med Phys. 2003 May; 30(5):823-31.
    View in: PubMed
    Score: 0.171
  24. A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002 Dec; 21(12):1552-63.
    View in: PubMed
    Score: 0.166
  25. Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms. IEEE Trans Med Imaging. 2022 01; 41(1):225-236.
    View in: PubMed
    Score: 0.156
  26. Computer-aided detection and diagnosis of breast cancer. Radiol Clin North Am. 2000 Jul; 38(4):725-40.
    View in: PubMed
    Score: 0.140
  27. Computer-aided diagnosis complements full-field digital mammography. Diagn Imaging (San Franc). 1999 Sep; 21(9):47-51, 75.
    View in: PubMed
    Score: 0.133
  28. Linkage of the ACR National Mammography Database to the Network of State Cancer Registries: Proof of Concept Evaluation by the ACR National Mammography Database Committee. J Am Coll Radiol. 2019 Jan; 16(1):8-14.
    View in: PubMed
    Score: 0.123
  29. 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.
    View in: PubMed
    Score: 0.114
  30. Comment on "Quantitative classification of breast tumors in digitized mammograms" [Med. Phys. 23, 1337-1345 (1996)]. Med Phys. 1997 Feb; 24(2):313, 315.
    View in: PubMed
    Score: 0.111
  31. Comparison of eye position versus computer identified microcalcification clusters on mammograms. Med Phys. 1997 Jan; 24(1):17-23.
    View in: PubMed
    Score: 0.110
  32. Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model. Med Phys. 2016 Jan; 43(1):159.
    View in: PubMed
    Score: 0.103
  33. Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT. Med Phys. 2015 Sep; 42(9):5479-89.
    View in: PubMed
    Score: 0.100
  34. Computer-aided detection of clustered microcalcifications on digital mammograms. Med Biol Eng Comput. 1995 Mar; 33(2):174-8.
    View in: PubMed
    Score: 0.097
  35. Computerized detection of clustered microcalcifications: evaluation of performance on mammograms from multiple centers. Radiographics. 1995 Mar; 15(2):443-52.
    View in: PubMed
    Score: 0.097
  36. Clinical use of digital mammography: the present and the prospects. J Digit Imaging. 1995 Feb; 8(1 Suppl 1):74-9.
    View in: PubMed
    Score: 0.096
  37. A computational model to generate simulated three-dimensional breast masses. Med Phys. 2015 Feb; 42(2):1098-118.
    View in: PubMed
    Score: 0.096
  38. Using breast radiographers' reports as a second opinion for radiologists' readings of microcalcifications in digital mammography. Br J Radiol. 2015 Mar; 88(1047):20140565.
    View in: PubMed
    Score: 0.096
  39. Analysis of perceived similarity between pairs of microcalcification clusters in mammograms. Med Phys. 2014 May; 41(5):051904.
    View in: PubMed
    Score: 0.092
  40. Effect of case selection on the performance of computer-aided detection schemes. Med Phys. 1994 Feb; 21(2):265-9.
    View in: PubMed
    Score: 0.090
  41. Algorithmic scatter correction in dual-energy digital mammography. Med Phys. 2013 Nov; 40(11):111919.
    View in: PubMed
    Score: 0.088
  42. Computer-aided detection of clustered microcalcifications: an improved method for grouping detected signals. Med Phys. 1993 Nov-Dec; 20(6):1661-6.
    View in: PubMed
    Score: 0.088
  43. Validation of a power-law noise model for simulating small-scale breast tissue. Phys Med Biol. 2013 Sep 07; 58(17):6011-27.
    View in: PubMed
    Score: 0.087
  44. Stereoscopic digital mammography: improved specificity and reduced rate of recall in a prospective clinical trial. Radiology. 2013 Jan; 266(1):81-8.
    View in: PubMed
    Score: 0.083
  45. 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.
    View in: PubMed
    Score: 0.082
  46. A statistically defined anthropomorphic software breast phantom. Med Phys. 2012 Jun; 39(6):3375-85.
    View in: PubMed
    Score: 0.080
  47. Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks. Med Phys. 1992 May-Jun; 19(3):555-60.
    View in: PubMed
    Score: 0.080
  48. Automated detection of mass lesions in dedicated breast CT: a preliminary study. Med Phys. 2012 Feb; 39(2):866-73.
    View in: PubMed
    Score: 0.078
  49. Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. Med Phys. 2009 Nov; 36(11):4920-32.
    View in: PubMed
    Score: 0.067
  50. Computer-aided detection evaluation methods are not created equal. Radiology. 2009 Jun; 251(3):634-6.
    View in: PubMed
    Score: 0.065
  51. Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study. Med Phys. 2008 Apr; 35(4):1486-93.
    View in: PubMed
    Score: 0.060
  52. Scanned-projection digital mammography. Med Phys. 1987 Sep-Oct; 14(5):717-27.
    View in: PubMed
    Score: 0.058
  53. Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms. Acad Radiol. 2007 Mar; 14(3):363-70.
    View in: PubMed
    Score: 0.056
  54. Computerized mass detection for digital breast tomosynthesis directly from the projection images. Med Phys. 2006 Feb; 33(2):482-91.
    View in: PubMed
    Score: 0.052
  55. Signal-to-noise properties of mammographic film-screen systems. Med Phys. 1985 Jan-Feb; 12(1):32-9.
    View in: PubMed
    Score: 0.048
  56. 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.
    View in: PubMed
    Score: 0.047
  57. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004 Oct; 23(10):1233-44.
    View in: PubMed
    Score: 0.047
  58. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis. Radiology. 2023 06; 307(5):e222639.
    View in: PubMed
    Score: 0.043
  59. Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening: A Preliminary Real-world Experience. J Breast Imaging. 2023 May 22; 5(3):258-266.
    View in: PubMed
    Score: 0.043
  60. Developing breast lesion detection algorithms for digital breast tomosynthesis: Leveraging false positive findings. Med Phys. 2022 Dec; 49(12):7596-7608.
    View in: PubMed
    Score: 0.041
  61. Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications. Radiology. 2001 Sep; 220(3):787-94.
    View in: PubMed
    Score: 0.038
  62. Radiologists' preferences for digital mammographic display. The International Digital Mammography Development Group. Radiology. 2000 Sep; 216(3):820-30.
    View in: PubMed
    Score: 0.036
  63. Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol. 1999 Aug; 31(2):97-109.
    View in: PubMed
    Score: 0.033
  64. Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol. 1999 Jan; 6(1):22-33.
    View in: PubMed
    Score: 0.032
  65. 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.
    View in: PubMed
    Score: 0.031
  66. Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms. Med Phys. 1998 Aug; 25(8):1502-6.
    View in: PubMed
    Score: 0.031
  67. Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. Med Phys. 1998 Jun; 25(6):949-56.
    View in: PubMed
    Score: 0.030
  68. Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications. IEEE Trans Med Imaging. 2017 05; 36(5):1162-1171.
    View in: PubMed
    Score: 0.028
  69. Density correction of peripheral breast tissue on digital mammograms. Radiographics. 1996 Nov; 16(6):1403-11.
    View in: PubMed
    Score: 0.027
  70. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms. Acad Radiol. 1996 Aug; 3(8):621-7.
    View in: PubMed
    Score: 0.027
  71. An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. Med Phys. 1996 Apr; 23(4):595-601.
    View in: PubMed
    Score: 0.026
  72. Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology. 1996 Mar; 198(3):671-8.
    View in: PubMed
    Score: 0.026
  73. Image feature analysis and computer-aided diagnosis in mammography: reduction of false-positive clustered microcalcifications using local edge-gradient analysis. Med Phys. 1995 Feb; 22(2):161-9.
    View in: PubMed
    Score: 0.024
  74. Automated segmentation of digitized mammograms. Acad Radiol. 1995 Jan; 2(1):1-9.
    View in: PubMed
    Score: 0.024
  75. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Med Phys. 1994 Apr; 21(4):517-24.
    View in: PubMed
    Score: 0.023
  76. Computer-aided diagnosis: development of automated schemes for quantitative analysis of radiographic images. Semin Ultrasound CT MR. 1992 Apr; 13(2):140-52.
    View in: PubMed
    Score: 0.020
  77. Anthropomorphic radiologic phantoms. Radiology. 1986 Feb; 158(2):550-2.
    View in: PubMed
    Score: 0.013
  78. Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications. Acad Radiol. 2006 Jan; 13(1):84-94.
    View in: PubMed
    Score: 0.013
  79. Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications. Med Phys. 2001 Sep; 28(9):1949-57.
    View in: PubMed
    Score: 0.010
  80. Potential usefulness of digital imaging in clinical diagnostic radiology: computer-aided diagnosis. J Digit Imaging. 1995 Feb; 8(1 Suppl 1):2-7.
    View in: PubMed
    Score: 0.006
  81. An "intelligent" workstation for computer-aided diagnosis. Radiographics. 1993 May; 13(3):647-56.
    View in: PubMed
    Score: 0.005
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.