These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

164 related articles for article (PubMed ID: 21153856)

  • 1. Computerized analysis of pneumoconiosis in digital chest radiography: effect of artificial neural network trained with power spectra.
    Okumura E; Kawashita I; Ishida T
    J Digit Imaging; 2011 Dec; 24(6):1126-32. PubMed ID: 21153856
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods.
    Okumura E; Kawashita I; Ishida T
    Radiol Phys Technol; 2014 Jul; 7(2):217-27. PubMed ID: 24414539
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.
    Okumura E; Kawashita I; Ishida T
    J Digit Imaging; 2017 Aug; 30(4):413-426. PubMed ID: 28108817
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme.
    Wu YC; Doi K; Giger ML; Metz CE; Zhang W
    J Digit Imaging; 1994 Nov; 7(4):196-207. PubMed ID: 7858017
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The development and evaluation of a computerized diagnosis scheme for pneumoconiosis on digital chest radiographs.
    Zhu B; Luo W; Li B; Chen B; Yang Q; Xu Y; Wu X; Chen H; Zhang K
    Biomed Eng Online; 2014 Oct; 13():141. PubMed ID: 25277489
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease.
    Ishida T; Katsuragawa S; Ashizawa K; MacMahon H; Doi K
    J Digit Imaging; 1998 Nov; 11(4):182-92. PubMed ID: 9848051
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification.
    Shiraishi J; Li Q; Suzuki K; Engelmann R; Doi K
    Med Phys; 2006 Jul; 33(7):2642-53. PubMed ID: 16898468
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks.
    Katsuragawa S; Doi K; MacMahon H; Monnier-Cholley L; Ishida T; Kobayashi T
    J Digit Imaging; 1997 Aug; 10(3):108-14. PubMed ID: 9268905
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Detection of lung nodules in digital chest radiographs using artificial neural networks: a pilot study.
    Wu YC; Doi K; Giger ML
    J Digit Imaging; 1995 May; 8(2):88-94. PubMed ID: 7612706
    [TBL] [Abstract][Full Text] [Related]  

  • 10. An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs.
    Yu P; Xu H; Zhu Y; Yang C; Sun X; Zhao J
    J Digit Imaging; 2011 Jun; 24(3):382-93. PubMed ID: 20174852
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Computerized analysis of interstitial disease in chest radiographs: improvement of geometric-pattern feature analysis.
    Ishida T; Katsuragawa S; Kobayashi T; MacMahon H; Doi K
    Med Phys; 1997 Jun; 24(6):915-24. PubMed ID: 9198027
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography.
    Wang X; Yu J; Zhu Q; Li S; Zhao Z; Yang B; Pu J
    Occup Environ Med; 2020 Sep; 77(9):597-602. PubMed ID: 32471837
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network.
    Suzuki K; Li F; Sone S; Doi K
    IEEE Trans Med Imaging; 2005 Sep; 24(9):1138-50. PubMed ID: 16156352
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Computerized detection of diffuse lung disease in MDCT: the usefulness of statistical texture features.
    Wang J; Li F; Doi K; Li Q
    Phys Med Biol; 2009 Nov; 54(22):6881-99. PubMed ID: 19864701
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN).
    Suzuki K; Abe H; MacMahon H; Doi K
    IEEE Trans Med Imaging; 2006 Apr; 25(4):406-16. PubMed ID: 16608057
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Support vector machine model for diagnosing pneumoconiosis based on wavelet texture features of digital chest radiographs.
    Zhu B; Chen H; Chen B; Xu Y; Zhang K
    J Digit Imaging; 2014 Feb; 27(1):90-7. PubMed ID: 23836078
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.
    Kim YG; Lee SM; Lee KH; Jang R; Seo JB; Kim N
    Eur Radiol; 2020 Sep; 30(9):4943-4951. PubMed ID: 32350657
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Development of an improved CAD scheme for automated detection of lung nodules in digital chest images.
    Xu XW; Doi K; Kobayashi T; MacMahon H; Giger ML
    Med Phys; 1997 Sep; 24(9):1395-403. PubMed ID: 9304567
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Effect of multiscale processing in digital chest radiography on automated detection of lung nodule with a computer assistance system.
    He Q; He W; Wang K; Ma D
    J Digit Imaging; 2008 Oct; 21 Suppl 1(Suppl 1):S164-70. PubMed ID: 18239963
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [Computerized classification of pneumoconiosis radiographs based on grey level co-occurrence matrices].
    Masumoto Y; Kawashita I; Okura Y; Nakajima M; Okumura E; Ishida T
    Nihon Hoshasen Gijutsu Gakkai Zasshi; 2011; 67(4):336-45. PubMed ID: 21532243
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.