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Pubmed for Handhelds

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Journal Abstract Search

270 related items for PubMed ID: 35066704

  • 21. [Comparison of fundus photography and fluorescein angiography in grading diabetic retinopathy].
    Gao LQ, Zhang F, Zhou HY, Yan W, Xiong Y, Wang GL.
    Zhonghua Yan Ke Za Zhi; 2008 Jan; 44(1):12-6. PubMed ID: 18510235
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  • 23. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.
    Bellemo V, Lim ZW, Lim G, Nguyen QD, Xie Y, Yip MYT, Hamzah H, Ho J, Lee XQ, Hsu W, Lee ML, Musonda L, Chandran M, Chipalo-Mutati G, Muma M, Tan GSW, Sivaprasad S, Menon G, Wong TY, Ting DSW.
    Lancet Digit Health; 2019 May; 1(1):e35-e44. PubMed ID: 33323239
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  • 24. A deep learning system for detecting diabetic retinopathy across the disease spectrum.
    Dai L, Wu L, Li H, Cai C, Wu Q, Kong H, Liu R, Wang X, Hou X, Liu Y, Long X, Wen Y, Lu L, Shen Y, Chen Y, Shen D, Yang X, Zou H, Sheng B, Jia W.
    Nat Commun; 2021 May 28; 12(1):3242. PubMed ID: 34050158
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  • 26. A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique.
    AbdelMaksoud E, Barakat S, Elmogy M.
    Med Biol Eng Comput; 2022 Jul 28; 60(7):2015-2038. PubMed ID: 35545738
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  • 29. Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.
    Van Craenendonck T, Elen B, Gerrits N, De Boever P.
    Transl Vis Sci Technol; 2020 Dec 28; 9(2):64. PubMed ID: 33403156
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  • 31. An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images.
    Akella PL, Kumar R.
    Graefes Arch Clin Exp Ophthalmol; 2024 Jan 28; 262(1):231-247. PubMed ID: 37548671
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  • 33. Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models.
    Alam MN, Yamashita R, Ramesh V, Prabhune T, Lim JI, Chan RVP, Hallak J, Leng T, Rubin D.
    Sci Rep; 2023 Apr 13; 13(1):6047. PubMed ID: 37055475
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  • 35. Deep learning-based analysis of infrared fundus photography for automated diagnosis of diabetic retinopathy with cataracts.
    Xue W, Zhang J, Ma Y, Hou J, Xiao F, Feng R, Zhao R, Zou H.
    J Cataract Refract Surg; 2023 Oct 01; 49(10):1043-1048. PubMed ID: 37488748
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  • 37. 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 01; 175():108459. PubMed ID: 38701588
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  • 38. Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy.
    Le D, Alam M, Yao CK, Lim JI, Hsieh YT, Chan RVP, Toslak D, Yao X.
    Transl Vis Sci Technol; 2020 Jul 01; 9(2):35. PubMed ID: 32855839
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  • 39. Clinical biomicroscopy versus fluorescein angiography: effectiveness and sensitivity in detecting diabetic retinopathy.
    Khalaf SS, Al-Bdour MD, Al-Till MI.
    Eur J Ophthalmol; 2007 Jul 01; 17(1):84-8. PubMed ID: 17294387
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