86 related articles for article (PubMed ID: 34880311)
1. Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions.
Al-Mukhtar M; Morad AH; Albadri M; Islam MDS
Sci Rep; 2021 Dec; 11(1):23631. PubMed ID: 34880311
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning.
Xu X; Liu D; Huang G; Wang M; Lei M; Jia Y
Comput Biol Med; 2024 May; 174():108428. PubMed ID: 38631117
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Artificial Humming Bird Optimization-Based Hybrid CNN-RNN for Accurate Exudate Classification from Fundus Images.
E D; S SP; R P; C BS
J Digit Imaging; 2023 Feb; 36(1):59-72. PubMed ID: 36241944
[TBL] [Abstract][Full Text] [Related]
8. SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading.
Huang Y; Lyu J; Cheng P; Tam R; Tang X
IEEE J Biomed Health Inform; 2024 May; 28(5):2806-2817. PubMed ID: 38319784
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: an ensemble learning approach.
Uppamma P; Bhattacharya S
Sci Rep; 2023 Oct; 13(1):18572. PubMed ID: 37903967
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric.
Chilukoti SV; Shan L; Tida VS; Maida AS; Hei X
BMC Med Inform Decis Mak; 2024 Feb; 24(1):37. PubMed ID: 38321416
[TBL] [Abstract][Full Text] [Related]
15. Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images.
Batool S; Gilani SO; Waris A; Iqbal KF; Khan NB; Khan MI; Eldin SM; Awwad FA
Sci Rep; 2023 Sep; 13(1):14462. PubMed ID: 37660096
[TBL] [Abstract][Full Text] [Related]
16. Source-free active domain adaptation for diabetic retinopathy grading based on ultra-wide-field fundus images.
Ran J; Zhang G; Xia F; Zhang X; Xie J; Zhang H
Comput Biol Med; 2024 May; 174():108418. PubMed ID: 38593641
[TBL] [Abstract][Full Text] [Related]
17. SG-MIAN: Self-guided multiple information aggregation network for image-level weakly supervised skin lesion segmentation.
Li Z; Zhang N; Gong H; Qiu R; Zhang W
Comput Biol Med; 2024 Mar; 170():107988. PubMed ID: 38232452
[TBL] [Abstract][Full Text] [Related]
18. Diabetic retinopathy detection using Bilayered Neural Network classification model with resubstitution validation.
Omer HK
MethodsX; 2024 Jun; 12():102705. PubMed ID: 38633420
[TBL] [Abstract][Full Text] [Related]
19. Fine-Grained Self-Supervised Learning with Jigsaw puzzles for medical image classification.
Park W; Ryu J
Comput Biol Med; 2024 May; 174():108460. PubMed ID: 38636330
[TBL] [Abstract][Full Text] [Related]
20. Collaborative learning of weakly-supervised domain adaptation for diabetic retinopathy grading on retinal images.
Cao P; Hou Q; Song R; Wang H; Zaiane O
Comput Biol Med; 2022 May; 144():105341. PubMed ID: 35279423
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]