138 related articles for article (PubMed ID: 30440485)
1. Fusing Results of Several Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema in Optical Coherence Tomography.
Chan GCY; Kamble R; Muller H; Shah SAA; Tang TB; Meriaudeau F
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():670-673. PubMed ID: 30440485
[TBL] [Abstract][Full Text] [Related]
2. Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images.
Sun Z; Sun Y
J Biomed Opt; 2019 May; 24(5):1-9. PubMed ID: 31111697
[TBL] [Abstract][Full Text] [Related]
3. Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.
Perdomo O; Rios H; Rodríguez FJ; Otálora S; Meriaudeau F; Müller H; González FA
Comput Methods Programs Biomed; 2019 Sep; 178():181-189. PubMed ID: 31416547
[TBL] [Abstract][Full Text] [Related]
4. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier.
Rasti R; Mehridehnavi A; Rabbani H; Hajizadeh F
J Biomed Opt; 2018 Mar; 23(3):1-10. PubMed ID: 29564864
[TBL] [Abstract][Full Text] [Related]
5. A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis.
Tang F; Wang X; Ran AR; Chan CKM; Ho M; Yip W; Young AL; Lok J; Szeto S; Chan J; Yip F; Wong R; Tang Z; Yang D; Ng DS; Chen LJ; Brelén M; Chu V; Li K; Lai THT; Tan GS; Ting DSW; Huang H; Chen H; Ma JH; Tang S; Leng T; Kakavand S; Mannil SS; Chang RT; Liew G; Gopinath B; Lai TYY; Pang CP; Scanlon PH; Wong TY; Tham CC; Chen H; Heng PA; Cheung CY
Diabetes Care; 2021 Sep; 44(9):2078-2088. PubMed ID: 34315698
[TBL] [Abstract][Full Text] [Related]
6. Fully automated detection of retinal disorders by image-based deep learning.
Li F; Chen H; Liu Z; Zhang X; Wu Z
Graefes Arch Clin Exp Ophthalmol; 2019 Mar; 257(3):495-505. PubMed ID: 30610422
[TBL] [Abstract][Full Text] [Related]
7. An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images.
Sidibé D; Sankar S; Lemaître G; Rastgoo M; Massich J; Cheung CY; Tan GSW; Milea D; Lamoureux E; Wong TY; Mériaudeau F
Comput Methods Programs Biomed; 2017 Feb; 139():109-117. PubMed ID: 28187882
[TBL] [Abstract][Full Text] [Related]
8. UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification.
Wang X; Tang F; Chen H; Luo L; Tang Z; Ran AR; Cheung CY; Heng PA
IEEE J Biomed Health Inform; 2020 Dec; 24(12):3431-3442. PubMed ID: 32248132
[TBL] [Abstract][Full Text] [Related]
9. Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning.
Chen YM; Huang WT; Ho WH; Tsai JT
BMC Bioinformatics; 2021 Nov; 22(Suppl 5):99. PubMed ID: 34749641
[TBL] [Abstract][Full Text] [Related]
10. Detection of Diabetic Macular Edema in Optical Coherence Tomography Image Using an Improved Level Set Algorithm.
Wang Z; Zhang W; Sun Y; Yao M; Yan B
Biomed Res Int; 2020; 2020():6974215. PubMed ID: 32420362
[TBL] [Abstract][Full Text] [Related]
11. Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach.
Ara RK; Matiolański A; Dziech A; Baran R; Domin P; Wieczorkiewicz A
Sensors (Basel); 2022 Jun; 22(13):. PubMed ID: 35808169
[TBL] [Abstract][Full Text] [Related]
12. Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism.
Sun Y; Zhang H; Yao X
J Biomed Opt; 2020 Sep; 25(9):. PubMed ID: 32940026
[TBL] [Abstract][Full Text] [Related]
13. Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis.
Li HY; Wang DX; Dong L; Wei WB
Eur J Ophthalmol; 2023 Jan; 33(1):278-290. PubMed ID: 35473414
[TBL] [Abstract][Full Text] [Related]
14. DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs.
Singh RK; Gorantla R
PLoS One; 2020; 15(2):e0220677. PubMed ID: 32040475
[TBL] [Abstract][Full Text] [Related]
15. AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.
Alqudah AM
Med Biol Eng Comput; 2020 Jan; 58(1):41-53. PubMed ID: 31728935
[TBL] [Abstract][Full Text] [Related]
16. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications.
Prahs P; Radeck V; Mayer C; Cvetkov Y; Cvetkova N; Helbig H; Märker D
Graefes Arch Clin Exp Ophthalmol; 2018 Jan; 256(1):91-98. PubMed ID: 29127485
[TBL] [Abstract][Full Text] [Related]
17. Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.
Arcadu F; Benmansour F; Maunz A; Michon J; Haskova Z; McClintock D; Adamis AP; Willis JR; Prunotto M
Invest Ophthalmol Vis Sci; 2019 Mar; 60(4):852-857. PubMed ID: 30821810
[TBL] [Abstract][Full Text] [Related]
18. Classification of optical coherence tomography images using a capsule network.
Tsuji T; Hirose Y; Fujimori K; Hirose T; Oyama A; Saikawa Y; Mimura T; Shiraishi K; Kobayashi T; Mizota A; Kotoku J
BMC Ophthalmol; 2020 Mar; 20(1):114. PubMed ID: 32192460
[TBL] [Abstract][Full Text] [Related]
19. Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning.
Schlegl T; Waldstein SM; Bogunovic H; Endstraßer F; Sadeghipour A; Philip AM; Podkowinski D; Gerendas BS; Langs G; Schmidt-Erfurth U
Ophthalmology; 2018 Apr; 125(4):549-558. PubMed ID: 29224926
[TBL] [Abstract][Full Text] [Related]
20. Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.
Hassan B; Hassan T; Li B; Ahmed R; Hassan O
Sensors (Basel); 2019 Jul; 19(13):. PubMed ID: 31284442
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
