459 related articles for article (PubMed ID: 21459533)
1. Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database.
Dietzel M; Baltzer PA; Dietzel A; Zoubi R; Gröschel T; Burmeister HP; Bogdan M; Kaiser WA
Eur J Radiol; 2012 Jul; 81(7):1508-13. PubMed ID: 21459533
[TBL] [Abstract][Full Text] [Related]
2. Application of artificial neural networks for the prediction of lymph node metastases to the ipsilateral axilla - initial experience in 194 patients using magnetic resonance mammography.
Dietzel M; Baltzer PA; Dietzel A; Vag T; Gröschel T; Gajda M; Camara O; Kaiser WA
Acta Radiol; 2010 Oct; 51(8):851-8. PubMed ID: 20707666
[TBL] [Abstract][Full Text] [Related]
3. Differential diagnosis of breast lesions 5 mm or less: is there a role for magnetic resonance imaging?
Dietzel M; Baltzer PA; Vag T; Gröschel T; Gajda M; Camara O; Kaiser WA
J Comput Assist Tomogr; 2010; 34(3):456-64. PubMed ID: 20498554
[TBL] [Abstract][Full Text] [Related]
4. A systematic comparison of two pulse sequences for edema assessment in MR-mammography.
Baltzer PA; Dietzel M; Gajda ; Camara O; Kaiser WA
Eur J Radiol; 2012 Jul; 81(7):1500-3. PubMed ID: 21481556
[TBL] [Abstract][Full Text] [Related]
5. Magnetic resonance mammography of invasive lobular versus ductal carcinoma: systematic comparison of 811 patients reveals high diagnostic accuracy irrespective of typing.
Dietzel M; Baltzer PA; Vag T; Gröschel T; Gajda M; Camara O; Kaiser WA
J Comput Assist Tomogr; 2010 Jul; 34(4):587-95. PubMed ID: 20657229
[TBL] [Abstract][Full Text] [Related]
6. Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon.
Baker JA; Kornguth PJ; Lo JY; Williford ME; Floyd CE
Radiology; 1995 Sep; 196(3):817-22. PubMed ID: 7644649
[TBL] [Abstract][Full Text] [Related]
7. Potential of MR mammography to predict tumor grading of invasive breast cancer.
Dietzel M; Baltzer PA; Vag T; Zoubi R; Gröschel T; Burmeister H; Gajda M; Runnebaum IB; Kaiser WA
Rofo; 2011 Sep; 183(9):826-33. PubMed ID: 21442559
[TBL] [Abstract][Full Text] [Related]
8. Advanced integrated technique in breast cancer thermography.
Ng EY; Kee EC
J Med Eng Technol; 2008; 32(2):103-14. PubMed ID: 17852648
[TBL] [Abstract][Full Text] [Related]
9. The necrosis sign in magnetic resonance-mammography: diagnostic accuracy in 1,084 histologically verified breast lesions.
Dietzel M; Baltzer PA; Vag T; Herzog A; Gajda M; Camara O; Kaiser WA
Breast J; 2010; 16(6):603-8. PubMed ID: 21070437
[TBL] [Abstract][Full Text] [Related]
10. Impact of missing data in evaluating artificial neural networks trained on complete data.
Markey MK; Tourassi GD; Margolis M; DeLong DM
Comput Biol Med; 2006 May; 36(5):516-25. PubMed ID: 15893745
[TBL] [Abstract][Full Text] [Related]
11. Magnetic resonance imaging of intraductal papillomas: typical findings and differential diagnosis.
Dietzel M; Kaiser C; Baltzer PA
J Comput Assist Tomogr; 2015; 39(2):176-84. PubMed ID: 25423553
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study.
Shimauchi A; Giger ML; Bhooshan N; Lan L; Pesce LL; Lee JK; Abe H; Newstead GM
Radiology; 2011 Mar; 258(3):696-704. PubMed ID: 21212365
[TBL] [Abstract][Full Text] [Related]
13. Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: comparison with empiric and quantitative kinetic parameters.
Szabó BK; Aspelin P; Wiberg MK
Acad Radiol; 2004 Dec; 11(12):1344-54. PubMed ID: 15596372
[TBL] [Abstract][Full Text] [Related]
14. Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering.
Chang YC; Huang YH; Huang CS; Chang PK; Chen JH; Chang RF
Magn Reson Imaging; 2012 Apr; 30(3):312-22. PubMed ID: 22245697
[TBL] [Abstract][Full Text] [Related]
15. Neural network analysis of breast cancer from MRI findings.
Abdolmaleki P; Buadu LD; Murayama S; Murakami J; Hashiguchi N; Yabuuchi H; Masuda K
Radiat Med; 1997; 15(5):283-93. PubMed ID: 9445150
[TBL] [Abstract][Full Text] [Related]
16. Clinical MR-mammography: are computer-assisted methods superior to visual or manual measurements for curve type analysis? A systematic approach.
Baltzer PA; Freiberg C; Beger S; Vag T; Dietzel M; Herzog AB; Gajda M; Camara O; Kaiser WA
Acad Radiol; 2009 Sep; 16(9):1070-6. PubMed ID: 19523854
[TBL] [Abstract][Full Text] [Related]
17. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.
Milenković J; Hertl K; Košir A; Zibert J; Tasič JF
Artif Intell Med; 2013 Jun; 58(2):101-14. PubMed ID: 23548472
[TBL] [Abstract][Full Text] [Related]
18. Observer study of a prototype clinical decision support system for breast cancer diagnosis using dynamic contrast-enhanced MRI.
Boroczky L; Simpson M; Abe H; Drysdale J
AJR Am J Roentgenol; 2013 Feb; 200(2):277-83. PubMed ID: 23345346
[TBL] [Abstract][Full Text] [Related]
19. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
Wu Y; Giger ML; Doi K; Vyborny CJ; Schmidt RA; Metz CE
Radiology; 1993 Apr; 187(1):81-7. PubMed ID: 8451441
[TBL] [Abstract][Full Text] [Related]
20. 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
Acad Radiol; 2009 Mar; 16(3):313-20. PubMed ID: 19201360
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
