BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

130 related articles for article (PubMed ID: 38631117)

  • 21. Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening.
    Wang H; Yuan G; Zhao X; Peng L; Wang Z; He Y; Qu C; Peng Z
    Comput Methods Programs Biomed; 2020 Jul; 191():105398. PubMed ID: 32092614
    [TBL] [Abstract][Full Text] [Related]  

  • 22. 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]  

  • 23. Microaneurysms detection in color fundus images using machine learning based on directional local contrast.
    Long S; Chen J; Hu A; Liu H; Chen Z; Zheng D
    Biomed Eng Online; 2020 Apr; 19(1):21. PubMed ID: 32295576
    [TBL] [Abstract][Full Text] [Related]  

  • 24. A novel image recuperation approach for diagnosing and ranking retinopathy disease level using diabetic fundus image.
    Krishnamoorthy S; Alli P
    PLoS One; 2015; 10(5):e0125542. PubMed ID: 25974230
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Diabetic retinopathy prediction based on vision transformer and modified capsule network.
    Oulhadj M; Riffi J; Khodriss C; Mahraz AM; Yahyaouy A; Abdellaoui M; Andaloussi IB; Tairi H
    Comput Biol Med; 2024 Jun; 175():108523. PubMed ID: 38701591
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.
    Hua CH; Huynh-The T; Kim K; Yu SY; Le-Tien T; Park GH; Bang J; Khan WA; Bae SH; Lee S
    Int J Med Inform; 2019 Dec; 132():103926. PubMed ID: 31605882
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy.
    Dupas B; Walter T; Erginay A; Ordonez R; Deb-Joardar N; Gain P; Klein JC; Massin P
    Diabetes Metab; 2010 Jun; 36(3):213-20. PubMed ID: 20219404
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.
    Albadr MAA; Ayob M; Tiun S; Al-Dhief FT; Hasan MK
    Front Public Health; 2022; 10():925901. PubMed ID: 35979449
    [TBL] [Abstract][Full Text] [Related]  

  • 29. A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet.
    Vijayan M; S V
    Diagnostics (Basel); 2023 Feb; 13(4):. PubMed ID: 36832262
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Simultaneous Diagnosis of Severity and Features of Diabetic Retinopathy in Fundus Photography Using Deep Learning.
    Wang J; Bai Y; Xia B
    IEEE J Biomed Health Inform; 2020 Dec; 24(12):3397-3407. PubMed ID: 32750975
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. 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]  

  • 33. Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features.
    Venkatesan R; Chandakkar P; Li B; Li HK
    Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():1462-5. PubMed ID: 23366177
    [TBL] [Abstract][Full Text] [Related]  

  • 34. IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.
    Porwal P; Pachade S; Kokare M; Deshmukh G; Son J; Bae W; Liu L; Wang J; Liu X; Gao L; Wu T; Xiao J; Wang F; Yin B; Wang Y; Danala G; He L; Choi YH; Lee YC; Jung SH; Li Z; Sui X; Wu J; Li X; Zhou T; Toth J; Baran A; Kori A; Chennamsetty SS; Safwan M; Alex V; Lyu X; Cheng L; Chu Q; Li P; Ji X; Zhang S; Shen Y; Dai L; Saha O; Sathish R; Melo T; Araújo T; Harangi B; Sheng B; Fang R; Sheet D; Hajdu A; Zheng Y; Mendonça AM; Zhang S; Campilho A; Zheng B; Shen D; Giancardo L; Quellec G; Mériaudeau F
    Med Image Anal; 2020 Jan; 59():101561. PubMed ID: 31671320
    [TBL] [Abstract][Full Text] [Related]  

  • 35. A review on computer-aided recent developments for automatic detection of diabetic retinopathy.
    Randive SN; Senapati RK; Rahulkar AD
    J Med Eng Technol; 2019 Feb; 43(2):87-99. PubMed ID: 31198073
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets.
    Saini M; Susan S
    Comput Biol Med; 2022 Oct; 149():105989. PubMed ID: 36037631
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images.
    Ganesan K; Martis RJ; Acharya UR; Chua CK; Min LC; Ng EY; Laude A
    Med Biol Eng Comput; 2014 Aug; 52(8):663-72. PubMed ID: 24958614
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images.
    Islam MR; Abdulrazak LF; Nahiduzzaman M; Goni MOF; Anower MS; Ahsan M; Haider J; Kowalski M
    Comput Biol Med; 2022 Jul; 146():105602. PubMed ID: 35569335
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images.
    Eladawi N; Elmogy M; Khalifa F; Ghazal M; Ghazi N; Aboelfetouh A; Riad A; Sandhu H; Schaal S; El-Baz A
    Med Phys; 2018 Oct; 45(10):4582-4599. PubMed ID: 30144102
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy.
    Berbar MA
    Health Inf Sci Syst; 2022 Dec; 10(1):14. PubMed ID: 35782197
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.