BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

293 related articles for article (PubMed ID: 32750920)

  • 1. ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach.
    Rodrigues EO; Conci A; Liatsis P
    IEEE J Biomed Health Inform; 2020 Dec; 24(12):3507-3519. PubMed ID: 32750920
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Hard Attention Net for Automatic Retinal Vessel Segmentation.
    Wang D; Haytham A; Pottenburgh J; Saeedi O; Tao Y
    IEEE J Biomed Health Inform; 2020 Dec; 24(12):3384-3396. PubMed ID: 32750941
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Multi-proportion channel ensemble model for retinal vessel segmentation.
    Tang P; Liang Q; Yan X; Zhang D; Coppola G; Sun W
    Comput Biol Med; 2019 Aug; 111():103352. PubMed ID: 31301636
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation.
    Yan Z; Yang X; Cheng KT
    IEEE Trans Biomed Eng; 2018 Sep; 65(9):1912-1923. PubMed ID: 29993396
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field.
    Chakravarty A; Sivaswamy J
    Comput Methods Programs Biomed; 2018 Oct; 165():235-250. PubMed ID: 30337078
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation.
    Khawaja A; Khan TM; Khan MAU; Nawaz SJ
    Sensors (Basel); 2019 Nov; 19(22):. PubMed ID: 31766276
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.
    Haleem MS; Han L; Hemert Jv; Fleming A; Pasquale LR; Silva PS; Song BJ; Aiello LP
    J Med Syst; 2016 Jun; 40(6):132. PubMed ID: 27086033
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.
    Wang Y; Zhang J; Cavichini M; Bartsch DG; Freeman WR; Nguyen TQ; An C
    IEEE Trans Image Process; 2021; 30():3167-3178. PubMed ID: 33600314
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation.
    Yan Z; Yang X; Cheng KT
    IEEE J Biomed Health Inform; 2019 Jul; 23(4):1427-1436. PubMed ID: 30281503
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Particle swarm optimization method for small retinal vessels detection on multiresolution fundus images.
    Khomri B; Christodoulidis A; Djerou L; Babahenini MC; Cheriet F
    J Biomed Opt; 2018 May; 23(5):1-13. PubMed ID: 29749141
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Retinal Vessel Segmentation, a Review of Classic and Deep Methods.
    Khandouzi A; Ariafar A; Mashayekhpour Z; Pazira M; Baleghi Y
    Ann Biomed Eng; 2022 Oct; 50(10):1292-1314. PubMed ID: 36008569
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Accurate Retinal Vessel Segmentation in Color Fundus Images via Fully Attention-Based Networks.
    Li K; Qi X; Luo Y; Yao Z; Zhou X; Sun M
    IEEE J Biomed Health Inform; 2021 Jun; 25(6):2071-2081. PubMed ID: 33001809
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Scale-space approximated convolutional neural networks for retinal vessel segmentation.
    Noh KJ; Park SJ; Lee S
    Comput Methods Programs Biomed; 2019 Sep; 178():237-246. PubMed ID: 31416552
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods.
    Hashemzadeh M; Adlpour Azar B
    Artif Intell Med; 2019 Apr; 95():1-15. PubMed ID: 30904129
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
    Liu Q; Zou B; Chen J; Ke W; Yue K; Chen Z; Zhao G
    Comput Med Imaging Graph; 2017 Jan; 55():78-86. PubMed ID: 27665058
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A Global and Local Enhanced Residual U-Net for Accurate Retinal Vessel Segmentation.
    Lian S; Li L; Lian G; Xiao X; Luo Z; Li S
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(3):852-862. PubMed ID: 31095493
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 15.