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Items: 1 to 20 of 110

1.

Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction.

García-Manso A, García-Orellana CJ, González-Velasco H, Gallardo-Caballero R, Macías MM.

Biomed Eng Online. 2013 Jan 10;12:2. doi: 10.1186/1475-925X-12-2.

2.

Study of the effect of breast tissue density on detection of masses in mammograms.

García-Manso A, García-Orellana CJ, González-Velasco HM, Gallardo-Caballero R, Macías-Macías M.

Comput Math Methods Med. 2013;2013:213794. doi: 10.1155/2013/213794. Epub 2013 Mar 21.

3.

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms.

Kim DH, Lee SH, Ro YM.

Biomed Eng Online. 2013;12 Suppl 1:S3. doi: 10.1186/1475-925X-12-S1-S3. Epub 2013 Dec 9.

4.

Computer-aided detection; the effect of training databases on detection of subtle breast masses.

Zheng B, Wang X, Lederman D, Tan J, Gur D.

Acad Radiol. 2010 Nov;17(11):1401-8. doi: 10.1016/j.acra.2010.06.009. Epub 2010 Jul 22.

5.

Dual system approach to computer-aided detection of breast masses on mammograms.

Wei J, Chan HP, Sahiner B, Hadjiiski LM, Helvie MA, Roubidoux MA, Zhou C, Ge J.

Med Phys. 2006 Nov;33(11):4157-68.

6.

Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers.

Park SC, Pu J, Zheng B.

Acad Radiol. 2009 Mar;16(3):266-74. doi: 10.1016/j.acra.2008.08.012.

7.

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis.

Mazurowski MA, Lo JY, Harrawood BP, Tourassi GD.

J Biomed Inform. 2011 Oct;44(5):815-23. doi: 10.1016/j.jbi.2011.04.008. Epub 2011 May 1.

8.

Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

Sharma S, Khanna P.

J Digit Imaging. 2015 Feb;28(1):77-90. doi: 10.1007/s10278-014-9719-7. Epub 2014 Jul 9.

9.

A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation.

Chu J, Min H, Liu L, Lu W.

Med Phys. 2015 Jul;42(7):3859-69. doi: 10.1118/1.4921612.

PMID:
26133587
10.

Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies.

Horsch A, Hapfelmeier A, Elter M.

Int J Comput Assist Radiol Surg. 2011 Nov;6(6):749-67. doi: 10.1007/s11548-011-0553-9. Epub 2011 Mar 30. Review.

PMID:
21448711
11.

Automated feature set selection and its application to MCC identification in digital mammograms for breast cancer detection.

Huang YJ, Chan DY, Cheng DC, Ho YJ, Tsai PP, Shen WC, Chen RF.

Sensors (Basel). 2013 Apr 11;13(4):4855-75. doi: 10.3390/s130404855.

12.

Investigation of optimal use of computer-aided detection systems: the role of the "machine" in decision making process.

Paquerault S, Hardy PT, Wersto N, Chen J, Smith RC.

Acad Radiol. 2010 Sep;17(9):1112-21. doi: 10.1016/j.acra.2010.04.010. Epub 2010 Jun 3.

PMID:
20605489
13.

An interactive system for computer-aided diagnosis of breast masses.

Wang X, Li L, Liu W, Xu W, Lederman D, Zheng B.

J Digit Imaging. 2012 Oct;25(5):570-9.

14.

Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.

Kooi T, van Ginneken B, Karssemeijer N, den Heeten A.

Med Phys. 2017 Mar;44(3):1017-1027. doi: 10.1002/mp.12110.

PMID:
28094850
15.

Computer-aided diagnosis of masses with full-field digital mammography.

Li L, Clark RA, Thomas JA.

Acad Radiol. 2002 Jan;9(1):4-12.

PMID:
11918357
16.

Image feature evaluation in two new mammography CAD prototypes.

Hapfelmeier A, Horsch A.

Int J Comput Assist Radiol Surg. 2011 Nov;6(6):721-35. doi: 10.1007/s11548-011-0549-5. Epub 2011 Mar 5.

PMID:
21380554
17.

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM.

de Nazaré Silva J, de Carvalho Filho AO, Corrêa Silva A, Cardoso de Paiva A, Gattass M.

J Digit Imaging. 2015 Jun;28(3):323-37. doi: 10.1007/s10278-014-9739-3.

18.

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

Melendez J, Sánchez CI, van Ginneken B, Karssemeijer N.

Med Phys. 2014 Aug;41(8):081904. doi: 10.1118/1.4885995.

PMID:
25086535
19.

Measures of radial correlation and trend for classification of breast masses in mammograms.

Casti P, Mencattini A, Salmeri M, Ancona A, Mangieri F, Rangayyan RM.

Conf Proc IEEE Eng Med Biol Soc. 2013;2013:6490-3. doi: 10.1109/EMBC.2013.6611041.

PMID:
24111228
20.

Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

Zyout I, Czajkowska J, Grzegorzek M.

Comput Med Imaging Graph. 2015 Dec;46 Pt 2:95-107. doi: 10.1016/j.compmedimag.2015.02.005. Epub 2015 Feb 24.

PMID:
25795630

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