BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

298 related articles for article (PubMed ID: 32972665)

  • 1. Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.
    Wu Z; Shi G; Chen Y; Shi F; Chen X; Coatrieux G; Yang J; Luo L; Li S
    Artif Intell Med; 2020 Aug; 108():101936. PubMed ID: 32972665
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Triple-DRNet: A triple-cascade convolution neural network for diabetic retinopathy grading using fundus images.
    Jian M; Chen H; Tao C; Li X; Wang G
    Comput Biol Med; 2023 Mar; 155():106631. PubMed ID: 36805216
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features.
    Harangi B; Toth J; Baran A; Hajdu A
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():2699-2702. PubMed ID: 31946452
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning.
    Sugeno A; Ishikawa Y; Ohshima T; Muramatsu R
    Comput Biol Med; 2021 Oct; 137():104795. PubMed ID: 34488028
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Joint Learning of Multi-Level Tasks for Diabetic Retinopathy Grading on Low-Resolution Fundus Images.
    Wang X; Xu M; Zhang J; Jiang L; Li L; He M; Wang N; Liu H; Wang Z
    IEEE J Biomed Health Inform; 2022 May; 26(5):2216-2227. PubMed ID: 34648460
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A convolutional neural network for the screening and staging of diabetic retinopathy.
    Shaban M; Ogur Z; Mahmoud A; Switala A; Shalaby A; Abu Khalifeh H; Ghazal M; Fraiwan L; Giridharan G; Sandhu H; El-Baz AS
    PLoS One; 2020; 15(6):e0233514. PubMed ID: 32569310
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
    Gayathri S; Gopi VP; Palanisamy P
    Phys Eng Sci Med; 2021 Sep; 44(3):639-653. PubMed ID: 34033015
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN.
    Durai DBJ; Jaya T
    Med Biol Eng Comput; 2023 Aug; 61(8):2091-2113. PubMed ID: 37338737
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.
    Romero-Oraá R; Herrero-Tudela M; López MI; Hornero R; García M
    Comput Methods Programs Biomed; 2024 Jun; 249():108160. PubMed ID: 38583290
    [TBL] [Abstract][Full Text] [Related]  

  • 10. MediDRNet: Tackling category imbalance in diabetic retinopathy classification with dual-branch learning and prototypical contrastive learning.
    Teng S; Wang B; Yang F; Yi X; Zhang X; Sun Y
    Comput Methods Programs Biomed; 2024 Aug; 253():108230. PubMed ID: 38810377
    [TBL] [Abstract][Full Text] [Related]  

  • 11. The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients.
    Xu Y; Wang Y; Liu B; Tang L; Lv L; Ke X; Ling S; Lu L; Zou H
    BMC Ophthalmol; 2019 Aug; 19(1):184. PubMed ID: 31412800
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning.
    Alyoubi WL; Abulkhair MF; Shalash WM
    Sensors (Basel); 2021 May; 21(11):. PubMed ID: 34073541
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention.
    Gu Z; Li Y; Wang Z; Kan J; Shu J; Wang Q
    Comput Intell Neurosci; 2023; 2023():1305583. PubMed ID: 36636467
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading.
    He A; Li T; Li N; Wang K; Fu H
    IEEE Trans Med Imaging; 2021 Jan; 40(1):143-153. PubMed ID: 32915731
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Robust Collaborative Learning of Patch-Level and Image-Level Annotations for Diabetic Retinopathy Grading From Fundus Image.
    Yang Y; Shang F; Wu B; Yang D; Wang L; Xu Y; Zhang W; Zhang T
    IEEE Trans Cybern; 2022 Nov; 52(11):11407-11417. PubMed ID: 33961571
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Multi-scale multi-attention network for diabetic retinopathy grading.
    Xia H; Long J; Song S; Tan Y
    Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38035368
    [No Abstract]   [Full Text] [Related]  

  • 17. Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection.
    Jinfeng G; Qummar S; Junming Z; Ruxian Y; Khan FG
    Comput Intell Neurosci; 2020; 2020():8864698. PubMed ID: 33381160
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images.
    Papadopoulos A; Topouzis F; Delopoulos A
    Sci Rep; 2021 Jul; 11(1):14326. PubMed ID: 34253799
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A novel approach for intelligent diagnosis and grading of diabetic retinopathy.
    Hai Z; Zou B; Xiao X; Peng Q; Yan J; Zhang W; Yue K
    Comput Biol Med; 2024 Apr; 172():108246. PubMed ID: 38471350
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Non-uniform Label Smoothing for Diabetic Retinopathy Grading from Retinal Fundus Images with Deep Neural Networks.
    Galdran A; Chelbi J; Kobi R; Dolz J; Lombaert H; Ben Ayed I; Chakor H
    Transl Vis Sci Technol; 2020 Jun; 9(2):34. PubMed ID: 32832207
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.