BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

191 related articles for article (PubMed ID: 36037631)

  • 1. 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]  

  • 2. Identifying Diabetic Retinopathy in the Human Eye: A Hybrid Approach Based on a Computer-Aided Diagnosis System Combined with Deep Learning.
    Atcı ŞY; Güneş A; Zontul M; Arslan Z
    Tomography; 2024 Feb; 10(2):215-230. PubMed ID: 38393285
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability.
    Zhou Y; Wang B; Huang L; Cui S; Shao L
    IEEE Trans Med Imaging; 2021 Mar; 40(3):818-828. PubMed ID: 33180722
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.
    Asiri N; Hussain M; Al Adel F; Alzaidi N
    Artif Intell Med; 2019 Aug; 99():101701. PubMed ID: 31606116
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.
    Fu Y; Wei Y; Chen S; Chen C; Zhou R; Li H; Qiu M; Xie J; Huang D
    Phys Med Biol; 2024 Feb; 69(4):. PubMed ID: 38271723
    [No Abstract]   [Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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; 60(7):2015-2038. PubMed ID: 35545738
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning.
    Erciyas A; Barışçı N
    Comput Math Methods Med; 2021; 2021():9928899. PubMed ID: 34194538
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.
    Mohanty C; Mahapatra S; Acharya B; Kokkoras F; Gerogiannis VC; Karamitsos I; Kanavos A
    Sensors (Basel); 2023 Jun; 23(12):. PubMed ID: 37420891
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.
    Nadeem MW; Goh HG; Hussain M; Liew SY; Andonovic I; Khan MA
    Sensors (Basel); 2022 Sep; 22(18):. PubMed ID: 36146130
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis.
    Cao P; Ren F; Wan C; Yang J; Zaiane O
    Comput Med Imaging Graph; 2018 Nov; 69():112-124. PubMed ID: 30237145
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.
    Nazir T; Irtaza A; Shabbir Z; Javed A; Akram U; Mahmood MT
    Artif Intell Med; 2019 Aug; 99():101695. PubMed ID: 31606114
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.
    Nazir T; Nawaz M; Rashid J; Mahum R; Masood M; Mehmood A; Ali F; Kim J; Kwon HY; Hussain A
    Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34450729
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Deep image mining for diabetic retinopathy screening.
    Quellec G; Charrière K; Boudi Y; Cochener B; Lamard M
    Med Image Anal; 2017 Jul; 39():178-193. PubMed ID: 28511066
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
    Tsiknakis N; Theodoropoulos D; Manikis G; Ktistakis E; Boutsora O; Berto A; Scarpa F; Scarpa A; Fotiadis DI; Marias K
    Comput Biol Med; 2021 Aug; 135():104599. PubMed ID: 34247130
    [TBL] [Abstract][Full Text] [Related]  

  • 20. FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy.
    Xu Y; Zhou Z; Li X; Zhang N; Zhang M; Wei P
    Biomed Res Int; 2021; 2021():6644071. PubMed ID: 33490274
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.