223 related articles for article (PubMed ID: 35916103)
1. Developing breast lesion detection algorithms for digital breast tomosynthesis: Leveraging false positive findings.
Hossain MB; Nishikawa RM; Lee J
Med Phys; 2022 Dec; 49(12):7596-7608. PubMed ID: 35916103
[TBL] [Abstract][Full Text] [Related]
2. Multi-domain features for reducing false positives in automated detection of clustered microcalcifications in digital breast tomosynthesis.
Zhang F; Wu S; Zhang C; Chen Q; Yang X; Jiang K; Zheng J
Med Phys; 2019 Mar; 46(3):1300-1308. PubMed ID: 30661242
[TBL] [Abstract][Full Text] [Related]
3. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.
Samala RK; Chan HP; Hadjiiski L; Helvie MA; Wei J; Cha K
Med Phys; 2016 Dec; 43(12):6654. PubMed ID: 27908154
[TBL] [Abstract][Full Text] [Related]
4. Detection of masses in digital breast tomosynthesis using complementary information of simulated projection.
Kim ST; Kim DH; Ro YM
Med Phys; 2015 Dec; 42(12):7043-58. PubMed ID: 26632059
[TBL] [Abstract][Full Text] [Related]
5. Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: a 3D approach.
Sahiner B; Chan HP; Hadjiiski LM; Helvie MA; Wei J; Zhou C; Lu Y
Med Phys; 2012 Jan; 39(1):28-39. PubMed ID: 22225272
[TBL] [Abstract][Full Text] [Related]
6. Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network.
Fan M; Li Y; Zheng S; Peng W; Tang W; Li L
Methods; 2019 Aug; 166():103-111. PubMed ID: 30771490
[TBL] [Abstract][Full Text] [Related]
7. A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.
Buda M; Saha A; Walsh R; Ghate S; Li N; Swiecicki A; Lo JY; Mazurowski MA
JAMA Netw Open; 2021 Aug; 4(8):e2119100. PubMed ID: 34398205
[TBL] [Abstract][Full Text] [Related]
8. Architectural distortion detection based on superior-inferior directional context and anatomic prior knowledge in digital breast tomosynthesis.
Li Y; He Z; Ma X; Zeng W; Liu J; Xu W; Xu Z; Wang S; Wen C; Zeng H; Wu J; Chen W; Lu Y
Med Phys; 2022 Jun; 49(6):3749-3768. PubMed ID: 35338787
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Multichannel response analysis on 2D projection views for detection of clustered microcalcifications in digital breast tomosynthesis.
Wei J; Chan HP; Hadjiiski LM; Helvie MA; Lu Y; Zhou C; Samala R
Med Phys; 2014 Apr; 41(4):041913. PubMed ID: 24694144
[TBL] [Abstract][Full Text] [Related]
11. Computer-aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume.
Samala RK; Chan HP; Lu Y; Hadjiiski L; Wei J; Sahiner B; Helvie MA
Med Phys; 2014 Feb; 41(2):021901. PubMed ID: 24506622
[TBL] [Abstract][Full Text] [Related]
12. Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms.
van Schie G; Wallis MG; Leifland K; Danielsson M; Karssemeijer N
Med Phys; 2013 Apr; 40(4):041902. PubMed ID: 23556896
[TBL] [Abstract][Full Text] [Related]
13. Combination of one-view digital breast tomosynthesis with one-view digital mammography versus standard two-view digital mammography: per lesion analysis.
Gennaro G; Hendrick RE; Toledano A; Paquelet JR; Bezzon E; Chersevani R; di Maggio C; La Grassa M; Pescarini L; Polico I; Proietti A; Baldan E; Pomerri F; Muzzio PC
Eur Radiol; 2013 Aug; 23(8):2087-94. PubMed ID: 23620367
[TBL] [Abstract][Full Text] [Related]
14. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis.
Konz N; Buda M; Gu H; Saha A; Yang J; Chledowski J; Park J; Witowski J; Geras KJ; Shoshan Y; Gilboa-Solomon F; Khapun D; Ratner V; Barkan E; Ozery-Flato M; Martí R; Omigbodun A; Marasinou C; Nakhaei N; Hsu W; Sahu P; Hossain MB; Lee J; Santos C; Przelaskowski A; Kalpathy-Cramer J; Bearce B; Cha K; Farahani K; Petrick N; Hadjiiski L; Drukker K; Armato SG; Mazurowski MA
JAMA Netw Open; 2023 Feb; 6(2):e230524. PubMed ID: 36821110
[TBL] [Abstract][Full Text] [Related]
15. Computerized mass detection for digital breast tomosynthesis directly from the projection images.
Reiser I; Nishikawa RM; Giger ML; Wu T; Rafferty EA; Moore R; Kopans DB
Med Phys; 2006 Feb; 33(2):482-91. PubMed ID: 16532956
[TBL] [Abstract][Full Text] [Related]
16. Computer-aided detection of masses in digital tomosynthesis mammography: comparison of three approaches.
Chan HP; Wei J; Zhang Y; Helvie MA; Moore RH; Sahiner B; Hadjiiski L; Kopans DB
Med Phys; 2008 Sep; 35(9):4087-95. PubMed ID: 18841861
[TBL] [Abstract][Full Text] [Related]
17. Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices.
Chan HP; Wu YT; Sahiner B; Wei J; Helvie MA; Zhang Y; Moore RH; Kopans DB; Hadjiiski L; Way T
Med Phys; 2010 Jul; 37(7):3576-86. PubMed ID: 20831065
[TBL] [Abstract][Full Text] [Related]
18. CAPNet: Context attention pyramid network for computer-aided detection of microcalcification clusters in digital breast tomosynthesis.
Wang J; Sun H; Jiang K; Cao W; Chen S; Zhu J; Yang X; Zheng J
Comput Methods Programs Biomed; 2023 Dec; 242():107831. PubMed ID: 37783114
[TBL] [Abstract][Full Text] [Related]
19. Classification of microcalcification clusters in digital breast tomosynthesis using ensemble convolutional neural network.
Xiao B; Sun H; Meng Y; Peng Y; Yang X; Chen S; Yan Z; Zheng J
Biomed Eng Online; 2021 Jul; 20(1):71. PubMed ID: 34320986
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]
