547 related articles for article (PubMed ID: 26233224)
1. External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.
Benndorf M; Burnside ES; Herda C; Langer M; Kotter E
Med Phys; 2015 Aug; 42(8):4987-96. PubMed ID: 26233224
[TBL] [Abstract][Full Text] [Related]
2. Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon.
Benndorf M; Kotter E; Langer M; Herda C; Wu Y; Burnside ES
Eur Radiol; 2015 Jun; 25(6):1768-75. PubMed ID: 25576230
[TBL] [Abstract][Full Text] [Related]
3. Breast cancer CADx based on BI-RAds descriptors from two mammographic views.
Gupta S; Chyn PF; Markey MK
Med Phys; 2006 Jun; 33(6):1810-7. PubMed ID: 16872088
[TBL] [Abstract][Full Text] [Related]
4. Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents.
Zhang J; Lo JY; Kuzmiak CM; Ghate SV; Yoon SC; Mazurowski MA
Med Phys; 2014 Sep; 41(9):091907. PubMed ID: 25186394
[TBL] [Abstract][Full Text] [Related]
5. Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning.
Carneiro G; Nascimento J; Bradley AP
IEEE Trans Med Imaging; 2017 Nov; 36(11):2355-2365. PubMed ID: 28920897
[TBL] [Abstract][Full Text] [Related]
6. Scoring System to Stratify Malignancy Risks for Mammographic Microcalcifications Based on Breast Imaging Reporting and Data System 5th Edition Descriptors.
Youk JH; Gweon HM; Son EJ; Eun NL; Choi EJ; Kim JA
Korean J Radiol; 2019 Dec; 20(12):1646-1652. PubMed ID: 31854152
[TBL] [Abstract][Full Text] [Related]
7. Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.
Mohamed AA; Luo Y; Peng H; Jankowitz RC; Wu S
J Digit Imaging; 2018 Aug; 31(4):387-392. PubMed ID: 28932980
[TBL] [Abstract][Full Text] [Related]
8. Correspondence in texture features between two mammographic views.
Gupta S; Markey MK
Med Phys; 2005 Jun; 32(6):1598-606. PubMed ID: 16013719
[TBL] [Abstract][Full Text] [Related]
9. Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis.
Huo Z; Giger ML; Vyborny CJ
IEEE Trans Med Imaging; 2001 Dec; 20(12):1285-92. PubMed ID: 11811828
[TBL] [Abstract][Full Text] [Related]
10. Determination of mammographic breast density using a deep convolutional neural network.
Ciritsis A; Rossi C; Vittoria De Martini I; Eberhard M; Marcon M; Becker AS; Berger N; Boss A
Br J Radiol; 2019 Jan; 92(1093):20180691. PubMed ID: 30209957
[TBL] [Abstract][Full Text] [Related]
11. 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. PubMed ID: 28094850
[TBL] [Abstract][Full Text] [Related]
12. Computer-aided classification of BI-RADS category 3 breast lesions.
Buchbinder SS; Leichter IS; Lederman RB; Novak B; Bamberger PN; Sklair-Levy M; Yarmish G; Fields SI
Radiology; 2004 Mar; 230(3):820-3. PubMed ID: 14739315
[TBL] [Abstract][Full Text] [Related]
13. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features.
Grimm LJ; Ghate SV; Yoon SC; Kuzmiak CM; Kim C; Mazurowski MA
Med Phys; 2014 Mar; 41(3):031909. PubMed ID: 24593727
[TBL] [Abstract][Full Text] [Related]
14. Fusion of k-Gabor features from medio-lateral-oblique and craniocaudal view mammograms for improved breast cancer diagnosis.
Sasikala S; Ezhilarasi M
J Cancer Res Ther; 2018; 14(5):1036-1041. PubMed ID: 30197344
[TBL] [Abstract][Full Text] [Related]
15. A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms.
Boumaraf S; Liu X; Ferkous C; Ma X
Biomed Res Int; 2020; 2020():7695207. PubMed ID: 32462017
[TBL] [Abstract][Full Text] [Related]
16. Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: automated measurement development for full field digital mammography.
Fowler EE; Sellers TA; Lu B; Heine JJ
Med Phys; 2013 Nov; 40(11):113502. PubMed ID: 24320473
[TBL] [Abstract][Full Text] [Related]
17. Provision of the DDSM mammography metadata in an accessible format.
Benndorf M; Herda C; Langer M; Kotter E
Med Phys; 2014 May; 41(5):051902. PubMed ID: 24784381
[TBL] [Abstract][Full Text] [Related]
18. Development and evaluation of a case-based reasoning classifier for prediction of breast biopsy outcome with BI-RADS lexicon.
Bilska-Wolak AO; Floyd CE
Med Phys; 2002 Sep; 29(9):2090-100. PubMed ID: 12349930
[TBL] [Abstract][Full Text] [Related]
19. Automated mammographic breast density estimation using a fully convolutional network.
Lee J; Nishikawa RM
Med Phys; 2018 Mar; 45(3):1178-1190. PubMed ID: 29363774
[TBL] [Abstract][Full Text] [Related]
20. Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions.
Tan M; Aghaei F; Wang Y; Zheng B
Phys Med Biol; 2017 Jan; 62(2):358-376. PubMed ID: 27997380
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
