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Connection

Robert Nishikawa to Female

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

0.808
  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: 0.046
  2. Organizational Breast Cancer Data Mart: A Solution for Assessing Outcomes of Imaging and Treatment. JCO Clin Cancer Inform. 2024 Apr; 8:e2300193.
    View in: PubMed
    Score: 0.045
  3. 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: 0.044
  4. AI in Screening Mammography: Use One Radiologist and Improve Double Reads. Radiology. 2023 11; 309(2):e232964.
    View in: PubMed
    Score: 0.043
  5. 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: 0.038
  6. Locally adaptive decision in detection of clustered microcalcifications in mammograms. Phys Med Biol. 2018 02 15; 63(4):045014.
    View in: PubMed
    Score: 0.029
  7. 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: 0.029
  8. 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: 0.023
  9. Estimating sensitivity and specificity for technology assessment based on observer studies. Acad Radiol. 2013 Jul; 20(7):825-30.
    View in: PubMed
    Score: 0.021
  10. 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: 0.020
  11. A comparison study of image features between FFDM and film mammogram images. Med Phys. 2012 Jul; 39(7):4386-94.
    View in: PubMed
    Score: 0.020
  12. 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: 0.019
  13. Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med Phys. 2012 Feb; 39(2):676-85.
    View in: PubMed
    Score: 0.019
  14. Clinical significance of serum growth-regulated oncogene alpha (GROalpha) in patients with gynecological cancer. Eur J Gynaecol Oncol. 2012; 33(2):138-41.
    View in: PubMed
    Score: 0.019
  15. 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: 0.019
  16. On the orientation of mammographic structure. Med Phys. 2011 Oct; 38(10):5303-6.
    View in: PubMed
    Score: 0.019
  17. Detection of clustered microcalcifications using spatial point process modeling. Phys Med Biol. 2011 Jan 07; 56(1):1-17.
    View in: PubMed
    Score: 0.018
  18. Computer-aided detection evaluation methods are not created equal. Radiology. 2009 Jun; 251(3):634-6.
    View in: PubMed
    Score: 0.016
  19. Comparison of power spectra for tomosynthesis projections and reconstructed images. Med Phys. 2009 May; 36(5):1753-8.
    View in: PubMed
    Score: 0.016
  20. Comparison of soft-copy and hard-copy reading for full-field digital mammography. Radiology. 2009 Apr; 251(1):41-9.
    View in: PubMed
    Score: 0.016
  21. 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: 0.014
  22. 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: 0.013
  23. 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: 0.011
  24. 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: 0.011
  25. 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: 0.011
  26. 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: 0.010
  27. A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002 Dec; 21(12):1552-63.
    View in: PubMed
    Score: 0.010
  28. 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: 0.010
  29. Computer-aided detection and diagnosis of breast cancer. Radiol Clin North Am. 2000 Jul; 38(4):725-40.
    View in: PubMed
    Score: 0.009
  30. Computer-aided diagnosis complements full-field digital mammography. Diagn Imaging (San Franc). 1999 Sep; 21(9):47-51, 75.
    View in: PubMed
    Score: 0.008
  31. 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: 0.008
  32. 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: 0.007
  33. Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT. Med Phys. 2017 May; 44(5):1846-1856.
    View in: PubMed
    Score: 0.007
  34. 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: 0.007
  35. Comparison of eye position versus computer identified microcalcification clusters on mammograms. Med Phys. 1997 Jan; 24(1):17-23.
    View in: PubMed
    Score: 0.007
  36. Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer. Breast Cancer Res. 2016 07 22; 18(1):76.
    View in: PubMed
    Score: 0.007
  37. 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: 0.006
  38. Computer-aided detection of clustered microcalcifications on digital mammograms. Med Biol Eng Comput. 1995 Mar; 33(2):174-8.
    View in: PubMed
    Score: 0.006
  39. Computerized detection of clustered microcalcifications: evaluation of performance on mammograms from multiple centers. Radiographics. 1995 Mar; 15(2):443-52.
    View in: PubMed
    Score: 0.006
  40. 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: 0.006
  41. 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: 0.006
  42. Analysis of perceived similarity between pairs of microcalcification clusters in mammograms. Med Phys. 2014 May; 41(5):051904.
    View in: PubMed
    Score: 0.006
  43. Effect of case selection on the performance of computer-aided detection schemes. Med Phys. 1994 Feb; 21(2):265-9.
    View in: PubMed
    Score: 0.006
  44. Algorithmic scatter correction in dual-energy digital mammography. Med Phys. 2013 Nov; 40(11):111919.
    View in: PubMed
    Score: 0.005
  45. 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: 0.005
  46. The potential of iodine for improving breast cancer diagnosis and treatment. Med Hypotheses. 2013 Jan; 80(1):94-8.
    View in: PubMed
    Score: 0.005
  47. 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: 0.005
  48. 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: 0.005
  49. 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: 0.005
  50. Automated detection of mass lesions in dedicated breast CT: a preliminary study. Med Phys. 2012 Feb; 39(2):866-73.
    View in: PubMed
    Score: 0.005
  51. 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.
    View in: PubMed
    Score: 0.004
  52. 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: 0.004
  53. Scanned-projection digital mammography. Med Phys. 1987 Sep-Oct; 14(5):717-27.
    View in: PubMed
    Score: 0.004
  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: 0.003
  55. 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: 0.003
  56. Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Trans Med Imaging. 2005 Oct; 24(10):1278-85.
    View in: PubMed
    Score: 0.003
  57. 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.
    View in: PubMed
    Score: 0.003
  58. Signal-to-noise properties of mammographic film-screen systems. Med Phys. 1985 Jan-Feb; 12(1):32-9.
    View in: PubMed
    Score: 0.003
  59. 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: 0.003
  60. 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: 0.003
  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: 0.002
  62. 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: 0.002
  63. Radiologists' preferences for digital mammographic display. The International Digital Mammography Development Group. Radiology. 2000 Sep; 216(3):820-30.
    View in: PubMed
    Score: 0.002
  64. Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol. 1999 Aug; 31(2):97-109.
    View in: PubMed
    Score: 0.002
  65. Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol. 1999 Jan; 6(1):22-33.
    View in: PubMed
    Score: 0.002
  66. 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: 0.002
  67. 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: 0.002
  68. 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: 0.002
  69. 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: 0.002
  70. 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: 0.002
  71. Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology. 1996 Mar; 198(3):671-8.
    View in: PubMed
    Score: 0.002
  72. Triple primary malignant neoplasms including a malignant brain tumor: report of two cases and review of the literature. Surg Neurol. 1996 Mar; 45(3):219-29.
    View in: PubMed
    Score: 0.002
  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: 0.001
  74. Automated segmentation of digitized mammograms. Acad Radiol. 1995 Jan; 2(1):1-9.
    View in: PubMed
    Score: 0.001
  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: 0.001
  76. Digital radiography. A useful clinical tool for computer-aided diagnosis by quantitative analysis of radiographic images. Acta Radiol. 1993 Sep; 34(5):426-39.
    View in: PubMed
    Score: 0.001
  77. [Treatment of metastatic brain tumor from renal cell carcinoma]. No Shinkei Geka. 1990 Oct; 18(10):935-8.
    View in: PubMed
    Score: 0.001
  78. Anthropomorphic radiologic phantoms. Radiology. 1986 Feb; 158(2):550-2.
    View in: PubMed
    Score: 0.001
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.