BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

181 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. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. 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]  

  • 18. 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]  

  • 19. Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.
    Xiao Z; Zhang X; Geng L; Zhang F; Wu J; Tong J; Ogunbona PO; Shan C
    Biomed Eng Online; 2017 Oct; 16(1):122. PubMed ID: 29073912
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN.
    Durai DBJ; Jaya T
    Med Biol Eng Comput; 2023 Aug; 61(8):2091-2113. PubMed ID: 37338737
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.