These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

147 related articles for article (PubMed ID: 35474556)

  • 1. Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives.
    Kundu S; Karale V; Ghorai G; Sarkar G; Ghosh S; Dhara AK
    J Digit Imaging; 2022 Oct; 35(5):1111-1119. PubMed ID: 35474556
    [TBL] [Abstract][Full Text] [Related]  

  • 2. DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images.
    Raza M; Naveed K; Akram A; Salem N; Afaq A; Madni HA; Khan MAU; Din MZ
    PLoS One; 2021; 16(12):e0261698. PubMed ID: 34972109
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. Automated Detection and Segmentation of Exudates for the Screening of Background Retinopathy.
    Kaur J; Mittal D; Malebary S; Nayak SR; Kumar D; Kumar M; Gagandeep ; Singh S
    J Healthc Eng; 2023; 2023():4537253. PubMed ID: 37483301
    [TBL] [Abstract][Full Text] [Related]  

  • 6. EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks.
    Wan C; Chen Y; Li H; Zheng B; Chen N; Yang W; Wang C; Li Y
    Dis Markers; 2021; 2021():6482665. PubMed ID: 34512815
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.
    Xu K; Feng D; Mi H
    Molecules; 2017 Nov; 22(12):. PubMed ID: 29168750
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images.
    Adal KM; van Etten PG; Martinez JP; Rouwen KW; Vermeer KA; van Vliet LJ
    IEEE Trans Biomed Eng; 2018 Jun; 65(6):1382-1390. PubMed ID: 28922110
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification.
    Dai B; Wu X; Bu W
    PLoS One; 2016; 11(8):e0161556. PubMed ID: 27564376
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Semantic segmentation of retinal exudates using a residual encoder-decoder architecture in diabetic retinopathy.
    Manan MA; Jinchao F; Khan TM; Yaqub M; Ahmed S; Chuhan IS
    Microsc Res Tech; 2023 Nov; 86(11):1443-1460. PubMed ID: 37194727
    [TBL] [Abstract][Full Text] [Related]  

  • 12. TDCAU-Net: retinal vessel segmentation using transformer dilated convolutional attention-based U-Net method.
    Li C; Li Z; Liu W
    Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38052089
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.
    Lim WX; Chen Z
    Med Biol Eng Comput; 2024 Aug; 62(8):2571-2583. PubMed ID: 38649629
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
    Liu Q; Zou B; Chen J; Ke W; Yue K; Chen Z; Zhao G
    Comput Med Imaging Graph; 2017 Jan; 55():78-86. PubMed ID: 27665058
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.
    Seoud L; Hurtut T; Chelbi J; Cheriet F; Langlois JM
    IEEE Trans Med Imaging; 2016 Apr; 35(4):1116-26. PubMed ID: 26701180
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Retinal image assessment using bi-level adaptive morphological component analysis.
    Javidi M; Harati A; Pourreza H
    Artif Intell Med; 2019 Aug; 99():101702. PubMed ID: 31606110
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Classification of images based on small local features: a case applied to microaneurysms in fundus retina images.
    Ordóñez PF; Cepeda CM; Garrido J; Chakravarty S
    J Med Imaging (Bellingham); 2017 Oct; 4(4):041309. PubMed ID: 29201938
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Diabetic retinopathy detection using red lesion localization and convolutional neural networks.
    Zago GT; Andreão RV; Dorizzi B; Teatini Salles EO
    Comput Biol Med; 2020 Jan; 116():103537. PubMed ID: 31747632
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.