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Journal Abstract Search

958 related items for PubMed ID: 30251934

  • 21. Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation.
    Majkowska A, Mittal S, Steiner DF, Reicher JJ, McKinney SM, Duggan GE, Eswaran K, Cameron Chen PH, Liu Y, Kalidindi SR, Ding A, Corrado GS, Tse D, Shetty S.
    Radiology; 2020 Feb; 294(2):421-431. PubMed ID: 31793848
    [Abstract] [Full Text] [Related]

  • 22. Deep learning-based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals.
    Lee JH, Park S, Hwang EJ, Goo JM, Lee WY, Lee S, Kim H, Andrews JR, Park CM.
    Eur Radiol; 2021 Feb; 31(2):1069-1080. PubMed ID: 32857202
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  • 23. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.
    Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, Patel BN, Yeom KW, Shpanskaya K, Blankenberg FG, Seekins J, Amrhein TJ, Mong DA, Halabi SS, Zucker EJ, Ng AY, Lungren MP.
    PLoS Med; 2018 Nov; 15(11):e1002686. PubMed ID: 30457988
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  • 24. Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study.
    Ueda D, Yamamoto A, Shimazaki A, Walston SL, Matsumoto T, Izumi N, Tsukioka T, Komatsu H, Inoue H, Kabata D, Nishiyama N, Miki Y.
    BMC Cancer; 2021 Oct 18; 21(1):1120. PubMed ID: 34663260
    [Abstract] [Full Text] [Related]

  • 25. Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.
    Chae KJ, Jin GY, Ko SB, Wang Y, Zhang H, Choi EJ, Choi H.
    Acad Radiol; 2020 Apr 18; 27(4):e55-e63. PubMed ID: 31780395
    [Abstract] [Full Text] [Related]

  • 26. Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs.
    Nam JG, Kim M, Park J, Hwang EJ, Lee JH, Hong JH, Goo JM, Park CM.
    Eur Respir J; 2021 May 18; 57(5):. PubMed ID: 33243843
    [Abstract] [Full Text] [Related]

  • 27. Extravalidation and reproducibility results of a commercial deep learning-based automatic detection algorithm for pulmonary nodules on chest radiographs at tertiary hospital.
    Koo YH, Shin KE, Park JS, Lee JW, Byun S, Lee H.
    J Med Imaging Radiat Oncol; 2021 Feb 18; 65(1):15-22. PubMed ID: 33090731
    [Abstract] [Full Text] [Related]

  • 28. Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction.
    Park S, Lee SM, Kim W, Park H, Jung KH, Do KH, Seo JB.
    Radiology; 2021 Apr 18; 299(1):211-219. PubMed ID: 33560190
    [Abstract] [Full Text] [Related]

  • 29. Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.
    Hirose T, Nitta N, Shiraishi J, Nagatani Y, Takahashi M, Murata K.
    Acad Radiol; 2008 Dec 18; 15(12):1505-12. PubMed ID: 19000867
    [Abstract] [Full Text] [Related]

  • 30. Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population.
    Lee JH, Sun HY, Park S, Kim H, Hwang EJ, Goo JM, Park CM.
    Radiology; 2020 Dec 18; 297(3):687-696. PubMed ID: 32960729
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  • 36. 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 18; 234(1):274-83. PubMed ID: 15537839
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  • 38. Efficacy of Ultrashort Echo Time Pulmonary MRI for Lung Nodule Detection and Lung-RADS Classification.
    Ohno Y, Takenaka D, Yoshikawa T, Yui M, Koyama H, Yamamoto K, Hamabuchi N, Shigemura C, Watanabe A, Ueda T, Ikeda H, Hattori H, Murayama K, Toyama H.
    Radiology; 2022 Mar 18; 302(3):697-706. PubMed ID: 34846203
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  • 39. Comparison of dual-energy subtraction and electronic bone suppression combined with computer-aided detection on chest radiographs: effect on human observers' performance in nodule detection.
    Szucs-Farkas Z, Schick A, Cullmann JL, Ebner L, Megyeri B, Vock P, Christe A.
    AJR Am J Roentgenol; 2013 May 18; 200(5):1006-13. PubMed ID: 23617482
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  • 40. Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images.
    Schalekamp S, van Ginneken B, Koedam E, Snoeren MM, Tiehuis AM, Wittenberg R, Karssemeijer N, Schaefer-Prokop CM.
    Radiology; 2014 Jul 18; 272(1):252-61. PubMed ID: 24635675
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