These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

185 related articles for article (PubMed ID: 34860847)

  • 21. Unsupervised domain adaptation multi-level adversarial learning-based crossing-domain retinal vessel segmentation.
    Liu J; Zhao J; Xiao J; Zhao G; Xu P; Yang Y; Gong S
    Comput Biol Med; 2024 Aug; 178():108759. PubMed ID: 38917530
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images.
    Lin J; Huang X; Zhou H; Wang Y; Zhang Q
    Med Image Anal; 2023 Oct; 89():102929. PubMed ID: 37598606
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier.
    Memari N; Ramli AR; Bin Saripan MI; Mashohor S; Moghbel M
    PLoS One; 2017; 12(12):e0188939. PubMed ID: 29228036
    [TBL] [Abstract][Full Text] [Related]  

  • 24. A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden Markov model.
    Zhou C; Zhang X; Chen H
    Comput Methods Programs Biomed; 2020 Apr; 187():105231. PubMed ID: 31786454
    [TBL] [Abstract][Full Text] [Related]  

  • 25. NFN+: A novel network followed network for retinal vessel segmentation.
    Wu Y; Xia Y; Song Y; Zhang Y; Cai W
    Neural Netw; 2020 Jun; 126():153-162. PubMed ID: 32222424
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Retinal blood vessel segmentation based on Densely Connected U-Net.
    Cheng YL; Ma MN; Zhang LJ; Jin CJ; Ma L; Zhou Y
    Math Biosci Eng; 2020 Apr; 17(4):3088-3108. PubMed ID: 32987518
    [TBL] [Abstract][Full Text] [Related]  

  • 27. MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.
    Jiang Y; Wu C; Wang G; Yao HX; Liu WH
    PLoS One; 2021; 16(7):e0253056. PubMed ID: 34252111
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images.
    Liu Y; Shen J; Yang L; Yu H; Bian G
    Comput Biol Med; 2023 Jan; 152():106341. PubMed ID: 36463794
    [TBL] [Abstract][Full Text] [Related]  

  • 29. A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model.
    Gegundez-Arias ME; Marin-Santos D; Perez-Borrero I; Vasallo-Vazquez MJ
    Comput Methods Programs Biomed; 2021 Jun; 205():106081. PubMed ID: 33882418
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Fast and efficient retinal blood vessel segmentation method based on deep learning network.
    Boudegga H; Elloumi Y; Akil M; Hedi Bedoui M; Kachouri R; Abdallah AB
    Comput Med Imaging Graph; 2021 Jun; 90():101902. PubMed ID: 33892389
    [TBL] [Abstract][Full Text] [Related]  

  • 31. A retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion.
    Li J; Gao G; Yang L; Liu Y
    Comput Biol Med; 2024 Apr; 172():108315. PubMed ID: 38503093
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Contrast Enhancement of RGB Retinal Fundus Images for Improved Segmentation of Blood Vessels Using Convolutional Neural Networks.
    Sule O; Viriri S
    J Digit Imaging; 2023 Apr; 36(2):414-432. PubMed ID: 36456839
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.
    Hassan M; Ali S; Kim JY; Saadia A; Sanaullah M; Alquhayz H; Safdar K
    Interdiscip Sci; 2023 Jun; 15(2):273-292. PubMed ID: 36611082
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Multi-Level Attention Network for Retinal Vessel Segmentation.
    Yuan Y; Zhang L; Wang L; Huang H
    IEEE J Biomed Health Inform; 2022 Jan; 26(1):312-323. PubMed ID: 34129508
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Iterative Vessel Segmentation of Fundus Images.
    Roychowdhury S; Koozekanani DD; Parhi KK
    IEEE Trans Biomed Eng; 2015 Jul; 62(7):1738-49. PubMed ID: 25700436
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Spatial attention U-Net model with Harris hawks optimization for retinal blood vessel and optic disc segmentation in fundus images.
    Kumar PR; Shilpa B; Jha RK; Chellibouina VS
    Int Ophthalmol; 2024 Aug; 44(1):359. PubMed ID: 39207645
    [TBL] [Abstract][Full Text] [Related]  

  • 37. DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images.
    Raza M; Naveed K; Akram A; Salem N; Afaq A; Madni HA; Khan MAU; Din MZ
    PLoS One; 2021; 16(12):e0261698. PubMed ID: 34972109
    [TBL] [Abstract][Full Text] [Related]  

  • 38. A High-Resolution Network with Strip Attention for Retinal Vessel Segmentation.
    Ye Z; Liu Y; Jing T; He Z; Zhou L
    Sensors (Basel); 2023 Nov; 23(21):. PubMed ID: 37960597
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation.
    Wang Z; Jia LV; Liang H
    Comput Biol Med; 2024 Aug; 178():108736. PubMed ID: 38878402
    [TBL] [Abstract][Full Text] [Related]  

  • 40. A novel retinal vessel detection approach based on multiple deep convolution neural networks.
    Guo Y; Budak Ü; Şengür A
    Comput Methods Programs Biomed; 2018 Dec; 167():43-48. PubMed ID: 30501859
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.