147 related articles for article (PubMed ID: 32746115)
1. Hierarchical Nonlocal Residual Networks for Image Quality Assessment of Pediatric Diffusion MRI With Limited and Noisy Annotations.
Liu S; Thung KH; Lin W; Shen D; Yap PT
IEEE Trans Med Imaging; 2020 Nov; 39(11):3691-3702. PubMed ID: 32746115
[TBL] [Abstract][Full Text] [Related]
2. Multi-stage Image Quality Assessment of Diffusion MRI via Semi-supervised Nonlocal Residual Networks.
Liu S; Thung KH; Lin W; Yap PT; Shen D;
Med Image Comput Comput Assist Interv; 2019; 11766():521-528. PubMed ID: 34447974
[TBL] [Abstract][Full Text] [Related]
3. Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks.
Liu S; Thung KH; Lin W; Yap PT; Shen D
IEEE Trans Image Process; 2020 May; ():. PubMed ID: 32396089
[TBL] [Abstract][Full Text] [Related]
4. No-reference quality assessment for image-based assessment of economically important tropical woods.
Rajagopal H; Mokhtar N; Tengku Mohmed Noor Izam TF; Wan Ahmad WK
PLoS One; 2020; 15(5):e0233320. PubMed ID: 32428043
[TBL] [Abstract][Full Text] [Related]
5. Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation.
Osman YBM; Li C; Huang W; Wang S
Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38035374
[No Abstract] [Full Text] [Related]
6. A deep learning-based automatic image quality assessment method for respiratory phase on computed tomography chest images.
Su J; Li M; Lin Y; Xiong L; Yuan C; Zhou Z; Yan K
Quant Imaging Med Surg; 2024 Mar; 14(3):2240-2254. PubMed ID: 38545050
[TBL] [Abstract][Full Text] [Related]
7. No-reference image quality assessment of magnetic resonance images with high-boost filtering and local features.
Oszust M; PiĆ³rkowski A; Obuchowicz R
Magn Reson Med; 2020 Sep; 84(3):1648-1660. PubMed ID: 32052485
[TBL] [Abstract][Full Text] [Related]
8. Entropy Based Data Expansion Method for Blind Image Quality Assessment.
Guan X; He L; Li M; Li F
Entropy (Basel); 2019 Dec; 22(1):. PubMed ID: 33285835
[TBL] [Abstract][Full Text] [Related]
9. Artifact- and content-specific quality assessment for MRI with image rulers.
Lei K; Syed AB; Zhu X; Pauly JM; Vasanawala SS
Med Image Anal; 2022 Apr; 77():102344. PubMed ID: 35091278
[TBL] [Abstract][Full Text] [Related]
10. Light mixed-supervised segmentation for 3D medical image data.
Yang H; Tan T; Tegzes P; Dong X; Tamada R; Ferenczi L; Avinash G
Med Phys; 2024 Jan; 51(1):167-178. PubMed ID: 37909833
[TBL] [Abstract][Full Text] [Related]
11. Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T.
Gagoski B; Xu J; Wighton P; Tisdall MD; Frost R; Lo WC; Golland P; van der Kouwe A; Adalsteinsson E; Grant PE
Magn Reson Med; 2022 Apr; 87(4):1914-1922. PubMed ID: 34888942
[TBL] [Abstract][Full Text] [Related]
12. Blind Deep S3D Image Quality Evaluation via Local to Global Feature Aggregation.
Heeseok Oh ; Sewoong Ahn ; Jongyoo Kim ; Sanghoon Lee
IEEE Trans Image Process; 2017 Oct; 26(10):4923-4936. PubMed ID: 28708557
[TBL] [Abstract][Full Text] [Related]
13. Image quality assessment for machine learning tasks using meta-reinforcement learning.
Saeed SU; Fu Y; Stavrinides V; Baum ZMC; Yang Q; Rusu M; Fan RE; Sonn GA; Noble JA; Barratt DC; Hu Y
Med Image Anal; 2022 May; 78():102427. PubMed ID: 35344824
[TBL] [Abstract][Full Text] [Related]
14. Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks.
Lu Q; Li Y; Ye C
Med Image Anal; 2021 Aug; 72():102094. PubMed ID: 34004493
[TBL] [Abstract][Full Text] [Related]
15. Deep learning-driven multi-view multi-task image quality assessment method for chest CT image.
Su J; Li M; Lin Y; Xiong L; Yuan C; Zhou Z; Yan K
Biomed Eng Online; 2023 Dec; 22(1):117. PubMed ID: 38057850
[TBL] [Abstract][Full Text] [Related]
16. Correlation between subjective and objective assessment of magnetic resonance (MR) images.
Chow LS; Rajagopal H; Paramesran R;
Magn Reson Imaging; 2016 Jul; 34(6):820-831. PubMed ID: 26969762
[TBL] [Abstract][Full Text] [Related]
17. Applicability Evaluation of Full-Reference Image Quality Assessment Methods for Computed Tomography Images.
Ohashi K; Nagatani Y; Yoshigoe M; Iwai K; Tsuchiya K; Hino A; Kida Y; Yamazaki A; Ishida T
J Digit Imaging; 2023 Dec; 36(6):2623-2634. PubMed ID: 37550519
[TBL] [Abstract][Full Text] [Related]
18. Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness.
Zeng R; Lin CY; Li Q; Jiang L; Skopec M; Fessler JA; Myers KJ
Med Phys; 2022 Feb; 49(2):836-853. PubMed ID: 34954845
[TBL] [Abstract][Full Text] [Related]
19. Image Quality Assessment Based on Local Linear Information and Distortion-Specific Compensation.
Hanli Wang ; Jie Fu ; Weisi Lin ; Sudeng Hu ; Jay Kuo CC; Lingxuan Zuo
IEEE Trans Image Process; 2017 Feb; 26(2):915-926. PubMed ID: 28113319
[TBL] [Abstract][Full Text] [Related]
20. CSPP-IQA: a multi-scale spatial pyramid pooling-based approach for blind image quality assessment.
Chen J; Qin F; Lu F; Guo L; Li C; Yan K; Zhou X
Neural Comput Appl; 2022 Oct; ():1-12. PubMed ID: 36276656
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]