146 related articles for article (PubMed ID: 33979759)
1. Towards improved breast mass detection using dual-view mammogram matching.
Yan Y; Conze PH; Lamard M; Quellec G; Cochener B; Coatrieux G
Med Image Anal; 2021 Jul; 71():102083. PubMed ID: 33979759
[TBL] [Abstract][Full Text] [Related]
2. DV-DCNN: Dual-view deep convolutional neural network for matching detected masses in mammograms.
AlGhamdi M; Abdel-Mottaleb M
Comput Methods Programs Biomed; 2021 Aug; 207():106152. PubMed ID: 34058629
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.
Jones MA; Sadeghipour N; Chen X; Islam W; Zheng B
Med Phys; 2023 Dec; 50(12):7670-7683. PubMed ID: 37083190
[TBL] [Abstract][Full Text] [Related]
5. YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms.
Aly GH; Marey M; El-Sayed SA; Tolba MF
Comput Methods Programs Biomed; 2021 Mar; 200():105823. PubMed ID: 33190942
[TBL] [Abstract][Full Text] [Related]
6. Influence of using manual or automatic breast density information in a mass detection CAD system.
Oliver A; Lladó X; Freixenet J; Martí R; Pérez E; Pont J; Zwiggelaar R
Acad Radiol; 2010 Jul; 17(7):877-83. PubMed ID: 20540910
[TBL] [Abstract][Full Text] [Related]
7. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.
Al-Masni MA; Al-Antari MA; Park JM; Gi G; Kim TY; Rivera P; Valarezo E; Choi MT; Han SM; Kim TS
Comput Methods Programs Biomed; 2018 Apr; 157():85-94. PubMed ID: 29477437
[TBL] [Abstract][Full Text] [Related]
8. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.
Al-Antari MA; Al-Masni MA; Choi MT; Han SM; Kim TS
Int J Med Inform; 2018 Sep; 117():44-54. PubMed ID: 30032964
[TBL] [Abstract][Full Text] [Related]
9. Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.
Al-Antari MA; Al-Masni MA; Kim TS
Adv Exp Med Biol; 2020; 1213():59-72. PubMed ID: 32030663
[TBL] [Abstract][Full Text] [Related]
10. Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques.
Baccouche A; Garcia-Zapirain B; Zheng Y; Elmaghraby AS
Comput Methods Programs Biomed; 2022 Jun; 221():106884. PubMed ID: 35594582
[TBL] [Abstract][Full Text] [Related]
11. Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.
Mendel K; Li H; Sheth D; Giger M
Acad Radiol; 2019 Jun; 26(6):735-743. PubMed ID: 30076083
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Evaluation of a computer-aided detection (CAD)-enhanced 2D synthetic mammogram: comparison with standard synthetic 2D mammograms and conventional 2D digital mammography.
James JJ; Giannotti E; Chen Y
Clin Radiol; 2018 Oct; 73(10):886-892. PubMed ID: 29970247
[TBL] [Abstract][Full Text] [Related]
14. Improvement of computerized mass detection on mammograms: fusion of two-view information.
Paquerault S; Petrick N; Chan HP; Sahiner B; Helvie MA
Med Phys; 2002 Feb; 29(2):238-47. PubMed ID: 11865995
[TBL] [Abstract][Full Text] [Related]
15. Combining two mammographic projections in a computer aided mass detection method.
van Engeland S; Karssemeijer N
Med Phys; 2007 Mar; 34(3):898-905. PubMed ID: 17441235
[TBL] [Abstract][Full Text] [Related]
16. Computer-aided detection in digital mammography: comparison of craniocaudal, mediolateral oblique, and mediolateral views.
Kim SJ; Moon WK; Cho N; Cha JH; Kim SM; Im JG
Radiology; 2006 Dec; 241(3):695-701. PubMed ID: 17114620
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Improved mammographic CAD performance using multi-view information: a Bayesian network framework.
Velikova M; Samulski M; Lucas PJ; Karssemeijer N
Phys Med Biol; 2009 Mar; 54(5):1131-47. PubMed ID: 19174596
[TBL] [Abstract][Full Text] [Related]
19. Computer-aided mass detection based on ipsilateral multiview mammograms.
Qian W; Song D; Lei M; Sankar R; Eikman E
Acad Radiol; 2007 May; 14(5):530-8. PubMed ID: 17434066
[TBL] [Abstract][Full Text] [Related]
20. Geometry-Based Pectoral Muscle Segmentation From MLO Mammogram Views.
Taghanaki SA; Liu Y; Miles B; Hamarneh G
IEEE Trans Biomed Eng; 2017 Nov; 64(11):2662-2671. PubMed ID: 28129144
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]