314 related articles for article (PubMed ID: 36268062)
1. Impact of color augmentation and tissue type in deep learning for hematoxylin and eosin image super resolution.
Manuel C; Zehnder P; Kaya S; Sullivan R; Hu F
J Pathol Inform; 2022; 13():100148. PubMed ID: 36268062
[TBL] [Abstract][Full Text] [Related]
2. Deep learning in computed tomography super resolution using multi-modality data training.
Fok WYR; Fieselmann A; Herbst M; Ritschl L; Kappler S; Saalfeld S
Med Phys; 2024 Apr; 51(4):2846-2860. PubMed ID: 37972365
[TBL] [Abstract][Full Text] [Related]
3. Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network.
Cui J; Gong K; Han P; Liu H; Li Q
Med Phys; 2022 Apr; 49(4):2373-2385. PubMed ID: 35048390
[TBL] [Abstract][Full Text] [Related]
4. A low-cost pathological image digitalization method based on 5 times magnification scanning.
Sun K; Gao Y; Xie T; Wang X; Yang Q; Chen L; Wang K; Yu G
Quant Imaging Med Surg; 2022 May; 12(5):2813-2829. PubMed ID: 35502389
[TBL] [Abstract][Full Text] [Related]
5. Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.
Rana A; Lowe A; Lithgow M; Horback K; Janovitz T; Da Silva A; Tsai H; Shanmugam V; Bayat A; Shah P
JAMA Netw Open; 2020 May; 3(5):e205111. PubMed ID: 32432709
[TBL] [Abstract][Full Text] [Related]
6. A Generative Adversarial Network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images.
Zhao M; Wei Y; Wong KKL
Magn Reson Imaging; 2022 Jan; 85():153-160. PubMed ID: 34699953
[TBL] [Abstract][Full Text] [Related]
7. Super-resolution and segmentation deep learning for breast cancer histopathology image analysis.
Juhong A; Li B; Yao CY; Yang CW; Agnew DW; Lei YL; Huang X; Piyawattanametha W; Qiu Z
Biomed Opt Express; 2023 Jan; 14(1):18-36. PubMed ID: 36698665
[TBL] [Abstract][Full Text] [Related]
8. MRI super-resolution reconstruction for MRI-guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model.
Chun J; Zhang H; Gach HM; Olberg S; Mazur T; Green O; Kim T; Kim H; Kim JS; Mutic S; Park JC
Med Phys; 2019 Sep; 46(9):4148-4164. PubMed ID: 31309585
[TBL] [Abstract][Full Text] [Related]
9. A novel hybrid generative adversarial network for CT and MRI super-resolution reconstruction.
Xiao Y; Chen C; Wang L; Yu J; Fu X; Zou Y; Lin Z; Wang K
Phys Med Biol; 2023 Jun; 68(13):. PubMed ID: 37285848
[No Abstract] [Full Text] [Related]
10. Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs.
Moran MBH; Faria MDB; Giraldi GA; Bastos LF; Conci A
Comput Biol Med; 2021 Feb; 129():104139. PubMed ID: 33271400
[TBL] [Abstract][Full Text] [Related]
11. Fast single image super-resolution using estimated low-frequency k-space data in MRI.
Luo J; Mou Z; Qin B; Li W; Yang F; Robini M; Zhu Y
Magn Reson Imaging; 2017 Jul; 40():1-11. PubMed ID: 28366758
[TBL] [Abstract][Full Text] [Related]
12. Super-resolution reconstruction, recognition, and evaluation of laser confocal images of hyperaccumulator
Li W; He D; Liu Y; Wang F; Huang F
Front Plant Sci; 2023; 14():1146485. PubMed ID: 37025152
[TBL] [Abstract][Full Text] [Related]
13. Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks.
Zhao M; Liu X; Liu H; Wong KKL
Comput Med Imaging Graph; 2020 Mar; 80():101698. PubMed ID: 31935666
[TBL] [Abstract][Full Text] [Related]
14. A Residual Dense Attention Generative Adversarial Network for Microscopic Image Super-Resolution.
Liu S; Weng X; Gao X; Xu X; Zhou L
Sensors (Basel); 2024 May; 24(11):. PubMed ID: 38894350
[TBL] [Abstract][Full Text] [Related]
15. Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization.
Li B; Keikhosravi A; Loeffler AG; Eliceiri KW
Med Image Anal; 2021 Feb; 68():101938. PubMed ID: 33359932
[TBL] [Abstract][Full Text] [Related]
16. Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study.
Mohammad-Rahimi H; Vinayahalingam S; Mahmoudinia E; Soltani P; Bergé SJ; Krois J; Schwendicke F
Diagnostics (Basel); 2023 Mar; 13(5):. PubMed ID: 36900140
[TBL] [Abstract][Full Text] [Related]
17. MRI super-resolution via realistic downsampling with adversarial learning.
Huang B; Xiao H; Liu W; Zhang Y; Wu H; Wang W; Yang Y; Yang Y; Miller GW; Li T; Cai J
Phys Med Biol; 2021 Oct; 66(20):. PubMed ID: 34474407
[TBL] [Abstract][Full Text] [Related]
18. Deep Learning Assisted Imaging Methods to Facilitate Access to Ophthalmic Telepathology.
Browne AW; Kim G; Vu AN; To JK; Minckler DS; Estopinal MDV; Rao NA; Curcio CA; Baldi PF
Ophthalmol Sci; 2024; 4(3):100450. PubMed ID: 38327842
[TBL] [Abstract][Full Text] [Related]
19. Improving resolution of panoramic radiographs: super-resolution concept.
Çelik ME; Mikaeili M; Çelik B
Dentomaxillofac Radiol; 2024 Apr; 53(4):240-247. PubMed ID: 38483289
[TBL] [Abstract][Full Text] [Related]
20. Multimodal-Boost: Multimodal Medical Image Super-Resolution Using Multi-Attention Network With Wavelet Transform.
Dharejo FA; Zawish M; Deeba F; Zhou Y; Dev K; Khowaja SA; Qureshi NMF
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(4):2420-2433. PubMed ID: 35849664
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]