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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1810 1
1813 3
1814 4
1944 1
1952 1
1954 1
1955 2
1956 2
1957 2
1958 1
1961 1
1965 1
1966 1
1968 1
1971 1
1972 1
1975 1
1978 1
1981 1
1984 1
1985 1
1986 1
1989 2
1990 5
1991 3
1992 5
1993 5
1994 1
1995 4
1996 6
1997 4
1998 3
1999 9
2000 8
2001 6
2002 8
2003 7
2004 17
2005 18
2006 34
2007 53
2008 45
2009 30
2010 11
2011 20
2012 20
2013 6
2014 9
2015 5
2016 8
2017 9
2018 8
2019 1
2020 9
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Article type
Publication date

Similar Articles for PMID: 18388216

367 results
Results by year
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Page 1
Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images.
Yamashita K, Yoshiura T, Arimura H, Mihara F, Noguchi T, Hiwatashi A, Togao O, Yamashita Y, Shono T, Kumazawa S, Higashida Y, Honda H. Yamashita K, et al. AJNR Am J Neuroradiol. 2008 Jun;29(6):1153-8. doi: 10.3174/ajnr.A1037. Epub 2008 Apr 3. AJNR Am J Neuroradiol. 2008. PMID: 18388216 Free article.
Differentiation of common large sellar-suprasellar masses effect of artificial neural network on radiologists' diagnosis performance.
Kitajima M, Hirai T, Katsuragawa S, Okuda T, Fukuoka H, Sasao A, Akter M, Awai K, Nakayama Y, Ikeda R, Yamashita Y, Yano S, Kuratsu J, Doi K. Kitajima M, et al. Acad Radiol. 2009 Mar;16(3):313-20. doi: 10.1016/j.acra.2008.09.015. Acad Radiol. 2009. PMID: 19201360
Neural network analysis of breast cancer from MRI findings.
Abdolmaleki P, Buadu LD, Murayama S, Murakami J, Hashiguchi N, Yabuuchi H, Masuda K. Abdolmaleki P, et al. Radiat Med. 1997 Sep-Oct;15(5):283-93. Radiat Med. 1997. PMID: 9445150
Computer-aided diagnosis of hepatic fibrosis: preliminary evaluation of MRI texture analysis using the finite difference method and an artificial neural network.
Kato H, Kanematsu M, Zhang X, Saio M, Kondo H, Goshima S, Fujita H. Kato H, et al. AJR Am J Roentgenol. 2007 Jul;189(1):117-22. doi: 10.2214/AJR.07.2070. AJR Am J Roentgenol. 2007. PMID: 17579160
Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema.
Server A, Kulle B, Maehlen J, Josefsen R, Schellhorn T, Kumar T, Langberg CW, Nakstad PH. Server A, et al. Acta Radiol. 2009 Jul;50(6):682-9. doi: 10.1080/02841850902933123. Acta Radiol. 2009. PMID: 19449234
Application of an artificial neural network to high-resolution CT: usefulness in differential diagnosis of diffuse lung disease.
Fukushima A, Ashizawa K, Yamaguchi T, Matsuyama N, Hayashi H, Kida I, Imafuku Y, Egawa A, Kimura S, Nagaoki K, Honda S, Katsuragawa S, Doi K, Hayashi K. Fukushima A, et al. AJR Am J Roentgenol. 2004 Aug;183(2):297-305. doi: 10.2214/ajr.183.2.1830297. AJR Am J Roentgenol. 2004. PMID: 15269016
Usefulness of artificial neural network for differential diagnosis of hepatic masses on CT images.
Matake K, Yoshimitsu K, Kumazawa S, Higashida Y, Irie H, Asayama Y, Nakayama T, Kakihara D, Katsuragawa S, Doi K, Honda H. Matake K, et al. Acad Radiol. 2006 Aug;13(8):951-62. doi: 10.1016/j.acra.2006.04.009. Acad Radiol. 2006. PMID: 16843847
Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images.
Juntu J, Sijbers J, De Backer S, Rajan J, Van Dyck D. Juntu J, et al. J Magn Reson Imaging. 2010 Mar;31(3):680-9. doi: 10.1002/jmri.22095. J Magn Reson Imaging. 2010. PMID: 20187212
Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging.
Hakyemez B, Erdogan C, Bolca N, Yildirim N, Gokalp G, Parlak M. Hakyemez B, et al. J Magn Reson Imaging. 2006 Oct;24(4):817-24. doi: 10.1002/jmri.20707. J Magn Reson Imaging. 2006. PMID: 16958061
Dynamic contrast enhanced MRI in the differential diagnosis of adrenal adenomas and malignant adrenal masses.
Inan N, Arslan A, Akansel G, Anik Y, Balci NC, Demirci A. Inan N, et al. Eur J Radiol. 2008 Jan;65(1):154-62. doi: 10.1016/j.ejrad.2007.03.012. Epub 2007 Apr 26. Eur J Radiol. 2008. PMID: 17466481
367 results
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