BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

164 related articles for article (PubMed ID: 36370579)

  • 21. Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.
    Burlina PM; Joshi N; Pacheco KD; Liu TYA; Bressler NM
    JAMA Ophthalmol; 2019 Mar; 137(3):258-264. PubMed ID: 30629091
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.
    Son J; Shin JY; Kim HD; Jung KH; Park KH; Park SJ
    Ophthalmology; 2020 Jan; 127(1):85-94. PubMed ID: 31281057
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Automated image quality appraisal through partial least squares discriminant analysis.
    Ramani RG; Shanthamalar JJ
    Int J Comput Assist Radiol Surg; 2022 Jul; 17(7):1367-1377. PubMed ID: 35650346
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Automated lesion detectors in retinal fundus images.
    Figueiredo IN; Kumar S; Oliveira CM; Ramos JD; Engquist B
    Comput Biol Med; 2015 Nov; 66():47-65. PubMed ID: 26378502
    [TBL] [Abstract][Full Text] [Related]  

  • 25. A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection.
    AbdelMaksoud E; Barakat S; Elmogy M
    Comput Biol Med; 2020 Nov; 126():104039. PubMed ID: 33068807
    [TBL] [Abstract][Full Text] [Related]  

  • 26. A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field.
    Chakravarty A; Sivaswamy J
    Comput Methods Programs Biomed; 2018 Oct; 165():235-250. PubMed ID: 30337078
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Automated techniques for blood vessels segmentation through fundus retinal images: A review.
    Akbar S; Sharif M; Akram MU; Saba T; Mahmood T; Kolivand M
    Microsc Res Tech; 2019 Feb; 82(2):153-170. PubMed ID: 30614150
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images.
    Köse C; Sevik U; Ikibaş C; Erdöl H
    Comput Methods Programs Biomed; 2012 Aug; 107(2):274-93. PubMed ID: 21757250
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine.
    Saha SK; Fernando B; Cuadros J; Xiao D; Kanagasingam Y
    J Digit Imaging; 2018 Dec; 31(6):869-878. PubMed ID: 29704086
    [TBL] [Abstract][Full Text] [Related]  

  • 30. A Review on the Extraction of Quantitative Retinal Microvascular Image Feature.
    Kipli K; Hoque ME; Lim LT; Mahmood MH; Sahari SK; Sapawi R; Rajaee N; Joseph A
    Comput Math Methods Med; 2018; 2018():4019538. PubMed ID: 30065780
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques.
    Akyol K; Şen B; Bayır Ş
    Comput Math Methods Med; 2016; 2016():6814791. PubMed ID: 27110272
    [TBL] [Abstract][Full Text] [Related]  

  • 32. 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]  

  • 33. SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation.
    Ullah Z; Usman M; Latif S; Khan A; Gwak J
    Sci Rep; 2023 Jun; 13(1):9087. PubMed ID: 37277554
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Automatic detection of microaneurysms in retinal fundus images.
    Wu B; Zhu W; Shi F; Zhu S; Chen X
    Comput Med Imaging Graph; 2017 Jan; 55():106-112. PubMed ID: 27595214
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
    Gulshan V; Peng L; Coram M; Stumpe MC; Wu D; Narayanaswamy A; Venugopalan S; Widner K; Madams T; Cuadros J; Kim R; Raman R; Nelson PC; Mega JL; Webster DR
    JAMA; 2016 Dec; 316(22):2402-2410. PubMed ID: 27898976
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema- A Review.
    Suriyasekeran K; Santhanamahalingam S; Duraisamy M
    Adv Exp Med Biol; 2021; 1307():357-373. PubMed ID: 32166636
    [TBL] [Abstract][Full Text] [Related]  

  • 37. 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]  

  • 38. 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]  

  • 39. Diabetic retinopathy techniques in retinal images: A review.
    Salamat N; Missen MMS; Rashid A
    Artif Intell Med; 2019 Jun; 97():168-188. PubMed ID: 30448367
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Optic disc detection and segmentation using saliency mask in retinal fundus images.
    Zaaboub N; Sandid F; Douik A; Solaiman B
    Comput Biol Med; 2022 Nov; 150():106067. PubMed ID: 36150251
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.