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Journal Abstract Search

818 related items for PubMed ID: 19034026

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  • 2. Commercially available computer-aided detection system for pulmonary nodules on thin-section images using 64 detectors-row CT: preliminary study of 48 cases.
    Yanagawa M, Honda O, Yoshida S, Ono Y, Inoue A, Daimon T, Sumikawa H, Mihara N, Johkoh T, Tomiyama N, Nakamura H.
    Acad Radiol; 2009 Aug; 16(8):924-33. PubMed ID: 19394873
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  • 4. Pulmonary nodules: sensitivity of maximum intensity projection versus that of volume rendering of 3D multidetector CT data.
    Peloschek P, Sailer J, Weber M, Herold CJ, Prokop M, Schaefer-Prokop C.
    Radiology; 2007 May; 243(2):561-9. PubMed ID: 17456878
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  • 7. Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection.
    Rubin GD, Lyo JK, Paik DS, Sherbondy AJ, Chow LC, Leung AN, Mindelzun R, Schraedley-Desmond PK, Zinck SE, Naidich DP, Napel S.
    Radiology; 2005 Jan; 234(1):274-83. PubMed ID: 15537839
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  • 14. A novel approach to nodule feature optimization on thin section thoracic CT.
    Samala R, Moreno W, You Y, Qian W.
    Acad Radiol; 2009 Apr; 16(4):418-27. PubMed ID: 19268853
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  • 15. Computer-aided detection of solid lung nodules on follow-up MDCT screening: evaluation of detection, tracking, and reading time.
    Beigelman-Aubry C, Raffy P, Yang W, Castellino RA, Grenier PA.
    AJR Am J Roentgenol; 2007 Oct; 189(4):948-55. PubMed ID: 17885070
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  • 19. A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).
    Suzuki K.
    Phys Med Biol; 2009 Sep 21; 54(18):S31-45. PubMed ID: 19687563
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