120 related articles for article (PubMed ID: 34002162)
1. Exploring CNN potential in discriminating benign and malignant calcifications in conventional and dual-energy FFDM: simulations and experimental observations.
Makeev A; Rodal G; Ghammraoui B; Badal A; Glick SJ
J Med Imaging (Bellingham); 2021 May; 8(3):033501. PubMed ID: 34002162
[No Abstract] [Full Text] [Related]
2. Classification of breast microcalcifications with GaAs photon-counting spectral mammography using an inverse problem approach.
Ghammraoui B; Bader S; Thuering T; Glick SJ
Biomed Phys Eng Express; 2023 Mar; 9(3):. PubMed ID: 36716475
[TBL] [Abstract][Full Text] [Related]
3. Using convolutional neural networks to discriminate between cysts and masses in Monte Carlo-simulated dual-energy mammography.
Makeev A; Toner B; Qian M; Badal A; Glick SJ
Med Phys; 2021 Aug; 48(8):4648-4655. PubMed ID: 34050965
[TBL] [Abstract][Full Text] [Related]
4. Investigating the feasibility of classifying breast microcalcifications using photon-counting spectral mammography: A simulation study.
Ghammraoui B; Glick SJ
Med Phys; 2017 Jun; 44(6):2304-2311. PubMed ID: 28332199
[TBL] [Abstract][Full Text] [Related]
5. Microcalcification detectability in breast CT images using CNN observers.
Lyu SH; Abbey CK; Hernandez AM; Boone JM
Med Phys; 2024 Feb; 51(2):933-945. PubMed ID: 38154070
[TBL] [Abstract][Full Text] [Related]
6. Classification of breast microcalcifications using dual-energy mammography.
Ghammraoui B; Makeev A; Zidan A; Alayoubi A; Glick SJ
J Med Imaging (Bellingham); 2019 Jan; 6(1):013502. PubMed ID: 30891465
[TBL] [Abstract][Full Text] [Related]
7. Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithmss using
Makeev A; Glick SJ
J Med Imaging (Bellingham); 2023 Sep; 10(5):053502. PubMed ID: 37808969
[TBL] [Abstract][Full Text] [Related]
8. Convolutional neural network -based phantom image scoring for mammography quality control.
Sundell VM; Mäkelä T; Vitikainen AM; Kaasalainen T
BMC Med Imaging; 2022 Dec; 22(1):216. PubMed ID: 36476319
[TBL] [Abstract][Full Text] [Related]
9. Comparative study in patients with microcalcifications: full-field digital mammography vs screen-film mammography.
Fischer U; Baum F; Obenauer S; Luftner-Nagel S; von Heyden D; Vosshenrich R; Grabbe E
Eur Radiol; 2002 Nov; 12(11):2679-83. PubMed ID: 12386757
[TBL] [Abstract][Full Text] [Related]
10. Performance of 2D Synthetic Mammography Versus Digital Mammography in the Detection of Microcalcifications at Screening.
Dodelzon K; Simon K; Dou E; Levy AD; Michaels AY; Askin G; Katzen JT
AJR Am J Roentgenol; 2020 Jun; 214(6):1436-1444. PubMed ID: 32255687
[No Abstract] [Full Text] [Related]
11. Comparison of the Detection Rate of Simulated Microcalcifications in Full-Field Digital Mammography, Digital Breast Tomosynthesis, and Synthetically Reconstructed 2-Dimensional Images Performed With 2 Different Digital X-ray Mammography Systems.
Peters S; Hellmich M; Stork A; Kemper J; Grinstein O; Püsken M; Stahlhut L; Kinner S; Maintz D; Krug KB
Invest Radiol; 2017 Apr; 52(4):206-215. PubMed ID: 27861206
[TBL] [Abstract][Full Text] [Related]
12. A comparison of full-field digital mammograms versus 2D synthesized mammograms for detection of microcalcifications on screening.
Wahab RA; Lee SJ; Zhang B; Sobel L; Mahoney MC
Eur J Radiol; 2018 Oct; 107():14-19. PubMed ID: 30292258
[TBL] [Abstract][Full Text] [Related]
13. Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.
Li X; Qin G; He Q; Sun L; Zeng H; He Z; Chen W; Zhen X; Zhou L
Eur Radiol; 2020 Feb; 30(2):778-788. PubMed ID: 31691121
[TBL] [Abstract][Full Text] [Related]
14. Microcalcifications Detected at Screening Mammography: Synthetic Mammography and Digital Breast Tomosynthesis versus Digital Mammography.
Lai YC; Ray KM; Lee AY; Hayward JH; Freimanis RI; Lobach IV; Joe BN
Radiology; 2018 Dec; 289(3):630-638. PubMed ID: 30277445
[TBL] [Abstract][Full Text] [Related]
15. Investigation of energy weighting using an energy discriminating photon counting detector for breast CT.
Kalluri KS; Mahd M; Glick SJ
Med Phys; 2013 Aug; 40(8):081923. PubMed ID: 23927337
[TBL] [Abstract][Full Text] [Related]
16. Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network.
Chan HP; Lo SC; Sahiner B; Lam KL; Helvie MA
Med Phys; 1995 Oct; 22(10):1555-67. PubMed ID: 8551980
[TBL] [Abstract][Full Text] [Related]
17. Effects of exposure equalization on image signal-to-noise ratios in digital mammography: a simulation study with an anthropomorphic breast phantom.
Liu X; Lai CJ; Whitman GJ; Geiser WR; Shen Y; Yi Y; Shaw CC
Med Phys; 2011 Dec; 38(12):6489-501. PubMed ID: 22149832
[TBL] [Abstract][Full Text] [Related]
18. Calcium oxalate crystals in breast biopsies. An overlooked form of microcalcification associated with benign breast disease.
Radi MJ
Arch Pathol Lab Med; 1989 Dec; 113(12):1367-9. PubMed ID: 2589947
[TBL] [Abstract][Full Text] [Related]
19. A novel physical anthropomorphic breast phantom for 2D and 3D x-ray imaging.
Ikejimba LC; Graff CG; Rosenthal S; Badal A; Ghammraoui B; Lo JY; Glick SJ
Med Phys; 2017 Feb; 44(2):407-416. PubMed ID: 27992059
[TBL] [Abstract][Full Text] [Related]
20. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.
Gao F; Wu T; Li J; Zheng B; Ruan L; Shang D; Patel B
Comput Med Imaging Graph; 2018 Dec; 70():53-62. PubMed ID: 30292910
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
