343 related articles for article (PubMed ID: 28788938)
1. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.
Nguyen T; Bui V; Lam V; Raub CB; Chang LC; Nehmetallah G
Opt Express; 2017 Jun; 25(13):15043-15057. PubMed ID: 28788938
[TBL] [Abstract][Full Text] [Related]
2. Automatic phase aberration compensation for digital holographic microscopy based on phase variation minimization.
Liu S; Lian Q; Qing Y; Xu Z
Opt Lett; 2018 Apr; 43(8):1870-1873. PubMed ID: 29652386
[TBL] [Abstract][Full Text] [Related]
3. Aberration-free digital holographic phase imaging using the derivative-based principal component analysis.
Lai X; Xiao S; Xu C; Fan S; Wei K
J Biomed Opt; 2021 Apr; 26(4):. PubMed ID: 33840164
[TBL] [Abstract][Full Text] [Related]
4. Multi-step phase aberration compensation method based on optimal principal component analysis and subsampling for digital holographic microscopy.
Zhang X; Sun J; Zhang Z; Fan Y; Chen Q; Zuo C
Appl Opt; 2019 Jan; 58(2):389-397. PubMed ID: 30645316
[TBL] [Abstract][Full Text] [Related]
5. Simple and fast spectral domain algorithm for quantitative phase imaging of living cells with digital holographic microscopy.
Min J; Yao B; Ketelhut S; Engwer C; Greve B; Kemper B
Opt Lett; 2017 Jan; 42(2):227-230. PubMed ID: 28081079
[TBL] [Abstract][Full Text] [Related]
6. Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning.
Xiao W; Xin L; Cao R; Wu X; Tian R; Che L; Sun L; Ferraro P; Pan F
Lab Chip; 2021 Apr; 21(7):1385-1394. PubMed ID: 33585849
[TBL] [Abstract][Full Text] [Related]
7. Accurate quantitative phase digital holographic microscopy with single- and multiple-wavelength telecentric and nontelecentric configurations.
Nguyen T; Nehmetallah G; Raub C; Mathews S; Aylo R
Appl Opt; 2016 Jul; 55(21):5666-83. PubMed ID: 27463923
[TBL] [Abstract][Full Text] [Related]
8. Quantitative phase imaging in digital holographic microscopy based on image inpainting using a two-stage generative adversarial network.
Ma S; Liu Q; Yu Y; Luo Y; Wang S
Opt Express; 2021 Aug; 29(16):24928-24946. PubMed ID: 34614837
[TBL] [Abstract][Full Text] [Related]
9. Aberration Estimation for Synthetic Aperture Digital Holographic Microscope Using Deep Neural Network.
Jeon H; Jung M; Lee G; Hahn J
Sensors (Basel); 2023 Nov; 23(22):. PubMed ID: 38005665
[TBL] [Abstract][Full Text] [Related]
10. Sequential processing of quantitative phase images for the study of cell behaviour in real-time digital holographic microscopy.
Zikmund T; Kvasnica L; Týč M; Křížová A; Colláková J; Chmelík R
J Microsc; 2014 Nov; 256(2):117-25. PubMed ID: 25142511
[TBL] [Abstract][Full Text] [Related]
11. Movies of cellular and sub-cellular motion by digital holographic microscopy.
Mann CJ; Yu L; Kim MK
Biomed Eng Online; 2006 Mar; 5():21. PubMed ID: 16556319
[TBL] [Abstract][Full Text] [Related]
12. Automatic aberration compensation for digital holographic microscopy based on bicubic downsampling and improved minimization of global phase gradients.
Yang J; Li F; Du J; Yang F; Yu S; Chen Q; Wang J; Zhang X; Sun S; Yan W
Opt Express; 2023 Oct; 31(22):36188-36201. PubMed ID: 38017773
[TBL] [Abstract][Full Text] [Related]
13. Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network.
Castaneda R; Trujillo C; Doblas A
Sensors (Basel); 2021 Dec; 21(23):. PubMed ID: 34884025
[TBL] [Abstract][Full Text] [Related]
14. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.
Sharma H; Zerbe N; Klempert I; Hellwich O; Hufnagl P
Comput Med Imaging Graph; 2017 Nov; 61():2-13. PubMed ID: 28676295
[TBL] [Abstract][Full Text] [Related]
15. High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks.
Yi F; Park S; Moon I
J Biomed Opt; 2021 Mar; 26(3):. PubMed ID: 33686845
[TBL] [Abstract][Full Text] [Related]
16. TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.
Rubin M; Stein O; Turko NA; Nygate Y; Roitshtain D; Karako L; Barnea I; Giryes R; Shaked NT
Med Image Anal; 2019 Oct; 57():176-185. PubMed ID: 31325721
[TBL] [Abstract][Full Text] [Related]
17. Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning.
Lam VK; Nguyen TC; Chung BM; Nehmetallah G; Raub CB
Cytometry A; 2018 Mar; 93(3):334-345. PubMed ID: 29283496
[TBL] [Abstract][Full Text] [Related]
18. Phase aberration compensation in digital holographic microscopy based on principal component analysis.
Zuo C; Chen Q; Qu W; Asundi A
Opt Lett; 2013 May; 38(10):1724-6. PubMed ID: 23938924
[TBL] [Abstract][Full Text] [Related]
19. Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.
Lin YH; Liao KY; Sung KB
J Biomed Opt; 2020 Nov; 25(11):. PubMed ID: 33188571
[TBL] [Abstract][Full Text] [Related]
20. In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning.
Belashov AV; Zhikhoreva AA; Belyaeva TN; Kornilova ES; Salova AV; Semenova IV; Vasyutinskii OS
J Opt Soc Am A Opt Image Sci Vis; 2020 Feb; 37(2):346-352. PubMed ID: 32118916
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
