| scholarly article | Q13442814 |
| P50 | author | Sina Farsiu | Q37829101 |
| P2093 | author name string | Scott W Cousins | |
| Michael J Allingham | |||
| Priyatham S Mettu | |||
| Kishan Govind | |||
| Reza Rasti | |||
| Sam Kavusi | |||
| P2860 | cites work | A feature-integration theory of attention | Q28281952 |
| Early and Long-Term Responses to Anti-Vascular Endothelial Growth Factor Therapy in Diabetic Macular Edema: Analysis of Protocol I Data | Q31131704 | ||
| Randomized trial evaluating ranibizumab plus prompt or deferred laser or triamcinolone plus prompt laser for diabetic macular edema | Q33567438 | ||
| Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. | Q33824639 | ||
| Global prevalence and major risk factors of diabetic retinopathy | Q35879130 | ||
| Aflibercept, Bevacizumab, or Ranibizumab for Diabetic Macular Edema: Two-Year Results from a Comparative Effectiveness Randomized Clinical Trial | Q35943499 | ||
| Aqueous cytokine levels are associated with reduced macular thickness after intravitreal ranibizumab for diabetic macular edema | Q36324073 | ||
| Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning | Q36961141 | ||
| Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection. | Q37172189 | ||
| Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography | Q37513827 | ||
| A Quantitative Approach to Predict Differential Effects of Anti-VEGF Treatment on Diffuse and Focal Leakage in Patients with Diabetic Macular Edema: A Pilot Study | Q37731896 | ||
| Diabetic retinopathy: current and new treatment options | Q37986772 | ||
| ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks | Q38371436 | ||
| BIOMARKERS OF NEOVASCULAR ACTIVITY IN AGE-RELATED MACULAR DEGENERATION USING OCT ANGIOGRAPHY. | Q38674684 | ||
| Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach. | Q38703395 | ||
| Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images | Q38802533 | ||
| Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. | Q42956754 | ||
| Effect of Adding Dexamethasone to Continued Ranibizumab Treatment in Patients With Persistent Diabetic Macular Edema: A DRCR Network Phase 2 Randomized Clinical Trial | Q45305404 | ||
| OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. | Q45944002 | ||
| Treatment of anti-vascular endothelial growth factor-resistant diabetic macular edema with dexamethasone intravitreal implant | Q47706263 | ||
| Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble. | Q52612107 | ||
| Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier. | Q52643185 | ||
| Degree of decrease in central retinal thickness predicts visual acuity response to intravitreal ranibizumab in diabetic macular edema. | Q54530134 | ||
| Clinically applicable deep learning for diagnosis and referral in retinal disease | Q56099055 | ||
| Biomarkers of optical coherence tomography in evaluating the treatment outcomes of neovascular age-related macular degeneration: a real-world study | Q61444274 | ||
| Convolutional Mixture of Experts Model: A Comparative Study on Automatic Macular Diagnosis in Retinal Optical Coherence Tomography Imaging | Q64266702 | ||
| Prediction of Anti-VEGF Response in Diabetic Macular Edema After 1 Injection | Q68641918 | ||
| Early Response to Anti-Vascular Endothelial Growth Factor and Two-Year Outcomes Among Eyes With Diabetic Macular Edema in Protocol T | Q90754573 | ||
| Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification | Q91578047 | ||
| Optical coherence tomography findings predictive of response to treatment in diabetic macular edema | Q91800069 | ||
| P4510 | describes a project that uses | scikit-image | Q22442795 |
| P433 | issue | 2 | |
| P921 | main subject | deep learning | Q197536 |
| diabetic macular edema | Q18558081 | ||
| P304 | page(s) | 1139-1152 | |
| P577 | publication date | 2020-01-28 | |
| P1433 | published in | Biomedical Optics Express | Q4915117 |
| P1476 | title | Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema | |
| P478 | volume | 11 |
| Q97422970 | Adversarial convolutional network for esophageal tissue segmentation on OCT images | cites work | P2860 |
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