BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

172 related articles for article (PubMed ID: 36322995)

  • 1. 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]  

  • 2. 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]  

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

  • 4. Vision Transformer-based recognition of diabetic retinopathy grade.
    Wu J; Hu R; Xiao Z; Chen J; Liu J
    Med Phys; 2021 Dec; 48(12):7850-7863. PubMed ID: 34693536
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multi-scale multi-attention network for diabetic retinopathy grading.
    Xia H; Long J; Song S; Tan Y
    Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38035368
    [No Abstract]   [Full Text] [Related]  

  • 6. 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
    [TBL] [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
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Fine-grained attention & knowledge-based collaborative network for diabetic retinopathy grading.
    Tian M; Wang H; Sun Y; Wu S; Tang Q; Zhang M
    Heliyon; 2023 Jul; 9(7):e17217. PubMed ID: 37449186
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. A new ultra-wide-field fundus dataset to diabetic retinopathy grading using hybrid preprocessing methods.
    Liu H; Teng L; Fan L; Sun Y; Li H
    Comput Biol Med; 2023 May; 157():106750. PubMed ID: 36931202
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A novel approach for intelligent diagnosis and grading of diabetic retinopathy.
    Hai Z; Zou B; Xiao X; Peng Q; Yan J; Zhang W; Yue K
    Comput Biol Med; 2024 Apr; 172():108246. PubMed ID: 38471350
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Understanding inherent image features in CNN-based assessment of diabetic retinopathy.
    Reguant R; Brunak S; Saha S
    Sci Rep; 2021 May; 11(1):9704. PubMed ID: 33958686
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Identifying the Key Components in ResNet-50 for Diabetic Retinopathy Grading from Fundus Images: A Systematic Investigation.
    Huang Y; Lin L; Cheng P; Lyu J; Tam R; Tang X
    Diagnostics (Basel); 2023 May; 13(10):. PubMed ID: 37238149
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.
    Bhulakshmi D; Rajput DS
    PeerJ Comput Sci; 2024; 10():e1947. PubMed ID: 38699206
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.