BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

160 related articles for article (PubMed ID: 38583290)

  • 1. 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]  

  • 2. DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.
    Araújo T; Aresta G; Mendonça L; Penas S; Maia C; Carneiro Â; Mendonça AM; Campilho A
    Med Image Anal; 2020 Jul; 63():101715. PubMed ID: 32434128
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Points of interest and visual dictionaries for automatic retinal lesion detection.
    Rocha A; Carvalho T; Jelinek HF; Goldenstein S; Wainer J
    IEEE Trans Biomed Eng; 2012 Aug; 59(8):2244-53. PubMed ID: 22665502
    [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. Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy.
    Romero-Oraá R; García M; Oraá-Pérez J; López-Gálvez MI; Hornero R
    Sensors (Basel); 2020 Nov; 20(22):. PubMed ID: 33207825
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy.
    Hassan D; Gill HM; Happe M; Bhatwadekar AD; Hajrasouliha AR; Janga SC
    Front Med (Lausanne); 2022; 9():1050436. PubMed ID: 36425113
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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; 9(2):64. PubMed ID: 33403156
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.
    Wang XN; Dai L; Li ST; Kong HY; Sheng B; Wu Q
    Curr Eye Res; 2020 Dec; 45(12):1550-1555. PubMed ID: 32410471
    [No Abstract]   [Full Text] [Related]  

  • 10. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Survey on recent developments in automatic detection of diabetic retinopathy.
    Bilal A; Sun G; Mazhar S
    J Fr Ophtalmol; 2021 Mar; 44(3):420-440. PubMed ID: 33526268
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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; 49(10):1043-1048. PubMed ID: 37488748
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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; 9(2):41. PubMed ID: 32855845
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis.
    Mohamed E; Elmohsen MA; Basha T
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():3898-3901. PubMed ID: 34892084
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading.
    Sahlsten J; Jaskari J; Kivinen J; Turunen L; Jaanio E; Hietala K; Kaski K
    Sci Rep; 2019 Jul; 9(1):10750. PubMed ID: 31341220
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep attentive convolutional neural network for automatic grading of imbalanced diabetic retinopathy in retinal fundus images.
    Li F; Tang S; Chen Y; Zou H
    Biomed Opt Express; 2022 Nov; 13(11):5813-5835. PubMed ID: 36733744
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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; 149():102782. PubMed ID: 38462283
    [TBL] [Abstract][Full Text] [Related]  

  • 19. CoT-XNet: contextual transformer with Xception network for diabetic retinopathy grading.
    Zhao S; Wu Y; Tong M; Yao Y; Qian W; Qi S
    Phys Med Biol; 2022 Dec; 67(24):. PubMed ID: 36322995
    [No Abstract]   [Full Text] [Related]  

  • 20. 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; 12(1):3242. PubMed ID: 34050158
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.