188 related articles for article (PubMed ID: 32305886)
1. Mammography Image Quality Assurance Using Deep Learning.
Kretz T; Mueller KR; Schaeffter T; Elster C
IEEE Trans Biomed Eng; 2020 Dec; 67(12):3317-3326. PubMed ID: 32305886
[TBL] [Abstract][Full Text] [Related]
2. Conversion factors between human and automatic readouts of CDMAM phantom images of CR mammography systems.
Figl M; Homolka P; Osanna-Elliott A; Semturs F; Kaar M; Hummel J
Phys Med Biol; 2016 Sep; 61(18):N514-N521. PubMed ID: 27580001
[TBL] [Abstract][Full Text] [Related]
3. Deep convolutional neural networks for mammography: advances, challenges and applications.
Abdelhafiz D; Yang C; Ammar R; Nabavi S
BMC Bioinformatics; 2019 Jun; 20(Suppl 11):281. PubMed ID: 31167642
[TBL] [Abstract][Full Text] [Related]
4. Evaluation of low-energy contrast-enhanced spectral mammography images by comparing them to full-field digital mammography using EUREF image quality criteria.
Lalji UC; Jeukens CR; Houben I; Nelemans PJ; van Engen RE; van Wylick E; Beets-Tan RG; Wildberger JE; Paulis LE; Lobbes MB
Eur Radiol; 2015 Oct; 25(10):2813-20. PubMed ID: 25813015
[TBL] [Abstract][Full Text] [Related]
5. New one-step model of breast tumor locating based on deep learning.
Tao C; Chen K; Han L; Peng Y; Li C; Hua Z; Lin J
J Xray Sci Technol; 2019; 27(5):839-856. PubMed ID: 31306148
[TBL] [Abstract][Full Text] [Related]
6. Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model.
Oh JH; Kim HG; Lee KM; Ryu CW
Eur J Radiol; 2022 Sep; 154():110369. PubMed ID: 35691109
[TBL] [Abstract][Full Text] [Related]
7. Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.
Squires S; Harkness EF; Mackenzie A; Evans DG; Howell SJ; Astley SM
Biomed Phys Eng Express; 2024 May; 10(4):. PubMed ID: 38701765
[No Abstract] [Full Text] [Related]
8. Ground-truth-free deep learning for artefacts reduction in 2D radial cardiac cine MRI using a synthetically generated dataset.
Chen D; Schaeffter T; Kolbitsch C; Kofler A
Phys Med Biol; 2021 Apr; 66(9):. PubMed ID: 33770783
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. Deep-learning-based direct inversion for material decomposition.
Gong H; Tao S; Rajendran K; Zhou W; McCollough CH; Leng S
Med Phys; 2020 Dec; 47(12):6294-6309. PubMed ID: 33020942
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study.
Clancy K; Aboutalib S; Mohamed A; Sumkin J; Wu S
J Digit Imaging; 2020 Oct; 33(5):1257-1265. PubMed ID: 32607908
[TBL] [Abstract][Full Text] [Related]
14. Deep Convolutional Neural Networks for breast cancer screening.
Chougrad H; Zouaki H; Alheyane O
Comput Methods Programs Biomed; 2018 Apr; 157():19-30. PubMed ID: 29477427
[TBL] [Abstract][Full Text] [Related]
15. Design and development of a phantom for tomosynthesis with potential for automated analysis via the cloud.
Goodenough D; Levy J; Olafsdottir H; Olafsson I
J Appl Clin Med Phys; 2018 May; 19(3):291-300. PubMed ID: 29508535
[TBL] [Abstract][Full Text] [Related]
16. Image generation by GAN and style transfer for agar plate image segmentation.
Andreini P; Bonechi S; Bianchini M; Mecocci A; Scarselli F
Comput Methods Programs Biomed; 2020 Feb; 184():105268. PubMed ID: 31891902
[TBL] [Abstract][Full Text] [Related]
17. White blood cells detection and classification based on regional convolutional neural networks.
Kutlu H; Avci E; Özyurt F
Med Hypotheses; 2020 Feb; 135():109472. PubMed ID: 31760248
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning-A feasibility study.
Badawy SM; Mohamed AEA; Hefnawy AA; Zidan HE; GadAllah MT; El-Banby GM
PLoS One; 2021; 16(5):e0251899. PubMed ID: 34014987
[TBL] [Abstract][Full Text] [Related]
20. Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.
Kim YJ; Kim KG
Yonsei Med J; 2022 Jan; 63(Suppl):S63-S73. PubMed ID: 35040607
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
