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Journal Abstract Search

170 related items for PubMed ID: 33961571

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. 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
    [Abstract] [Full Text] [Related]

  • 3. 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
    [Abstract] [Full Text] [Related]

  • 4. 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
    [Abstract] [Full Text] [Related]

  • 5. 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
    [Abstract] [Full Text] [Related]

  • 6. CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading.
    Wei H, Shi P, Miao J, Zhang M, Bai G, Qiu J, Liu F, Yuan W.
    Comput Biol Med; 2024 Jun; 175():108459. PubMed ID: 38701588
    [Abstract] [Full Text] [Related]

  • 7. 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
    [Abstract] [Full Text] [Related]

  • 8. 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
    [Abstract] [Full Text] [Related]

  • 9. 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 12; 11(1):14326. PubMed ID: 34253799
    [Abstract] [Full Text] [Related]

  • 10. 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 12; 61(8):2091-2113. PubMed ID: 37338737
    [Abstract] [Full Text] [Related]

  • 11. Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy.
    Tseng VS, Chen CL, Liang CM, Tai MC, Liu JT, Wu PY, Deng MS, Lee YW, Huang TY, Chen YH.
    Transl Vis Sci Technol; 2020 Jul 12; 9(2):41. PubMed ID: 32855845
    [Abstract] [Full Text] [Related]

  • 12. An interpretable dual attention network for diabetic retinopathy grading: IDANet.
    Bhati A, Gour N, Khanna P, Ojha A, Werghi N.
    Artif Intell Med; 2024 Mar 12; 149():102782. PubMed ID: 38462283
    [Abstract] [Full Text] [Related]

  • 13. 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 12; 253():108230. PubMed ID: 38810377
    [Abstract] [Full Text] [Related]

  • 14. Source-free active domain adaptation for diabetic retinopathy grading based on ultra-wide-field fundus images.
    Ran J, Zhang G, Xia F, Zhang X, Xie J, Zhang H.
    Comput Biol Med; 2024 May 12; 174():108418. PubMed ID: 38593641
    [Abstract] [Full Text] [Related]

  • 15. Image Quality Assessment Guided Collaborative Learning of Image Enhancement and Classification for Diabetic Retinopathy Grading.
    Hou Q, Cao P, Jia L, Chen L, Yang J, Zaiane OR.
    IEEE J Biomed Health Inform; 2022 Dec 23; PP():. PubMed ID: 37015399
    [Abstract] [Full Text] [Related]

  • 16. Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.
    Farooq MS, Arooj A, Alroobaea R, Baqasah AM, Jabarulla MY, Singh D, Sardar R.
    Sensors (Basel); 2022 Feb 24; 22(5):. PubMed ID: 35270949
    [Abstract] [Full Text] [Related]

  • 17. Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
    Gayathri S, Gopi VP, Palanisamy P.
    Phys Eng Sci Med; 2021 Sep 24; 44(3):639-653. PubMed ID: 34033015
    [Abstract] [Full Text] [Related]

  • 18. Semantic-Oriented Visual Prompt Learning for Diabetic Retinopathy Grading on Fundus Images.
    Zhang Y, Ma X, Huang K, Li M, Heng PA.
    IEEE Trans Med Imaging; 2024 Aug 24; 43(8):2960-2969. PubMed ID: 38564346
    [Abstract] [Full Text] [Related]

  • 19. Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis.
    Maji D, Sekh AA.
    J Med Syst; 2020 Sep 01; 44(10):180. PubMed ID: 32870389
    [Abstract] [Full Text] [Related]

  • 20. Augmentation-Consistent Clustering Network for Diabetic Retinopathy Grading with Fewer Annotations.
    Zhang G, Li K, Chen Z, Sun L, Zhang J, Pan X.
    J Healthc Eng; 2022 Sep 01; 2022():4246239. PubMed ID: 35388319
    [Abstract] [Full Text] [Related]

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