Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends | Paper Citation Record

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Unresolved cited work

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Ertel, Introduction to artificial intelligence

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Unresolved cited work

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Artificial intelligence in healthcare: Past, present and future,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends The role of artificial intelligence in healthcare: A structured literature review,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Machine learning: Trends, per- spectives, and prospects,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Deep learning,

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This paper cites Gradient-based learning applied to document recognition,.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Gradient-based learning applied to document recognition,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Notes on convolutional neural networks,

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This paper cites Imagenet classifica- tion with deep convolutional neural networks,.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Imagenet classifica- tion with deep convolutional neural networks,

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This paper cites A survey of con- volutional neural networks: Analysis, applications, and prospects,.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends A survey of con- volutional neural networks: Analysis, applications, and prospects,

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This paper cites Learning rep- resentations by back-propagating errors,.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Learning rep- resentations by back-propagating errors,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Recurrent Neural Networks (RNNs): A gentle Introduction and Overview

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Long short-term memory,

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This paper cites On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Representation learning: A review and new perspectives,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Attention is all you need,

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends An image is worth 16x16 words: Transformers for image recognition at scale,

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This paper cites Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes,.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes,

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This paper cites Clinically applicable deep learning for diagnosis and referral in retinal disease,.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Clinically applicable deep learning for diagnosis and referral in retinal disease,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Machine learning of the progression of intermediate age-related macular degeneration based on oct imaging,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Artificial intelligence and deep learning in ophthalmology,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Medical image analysis using deep learning algorithms,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Evaluation of explainable deep learn- ing methods for ophthalmic diagnosis,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Can we open the black box of ai?

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Deep learning to improve diagnosis must also not do harm,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Explainable and interpretable artificial intelligence in medicine: A systematic bibliometric review,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Privacy-preserving deep learning in medical informatics: Applications, challenges, and solutions,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Privacy-Preserving Distributed Deep Learning for Clinical Data

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Diabetic retinopathy,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Retinal imaging and image analysis,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Xgboost: A scalable tree boosting sys- tem,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Ngboost: Natural gradient boosting for probabilistic prediction,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends InterpretML: A Unified Framework for Machine Learning Interpretability

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Ai-human hybrid workflow enhances teleophthalmology for the detection of diabetic retinopathy,

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This paper cites A feasibility study of diabetic retinopathy detection in type ii diabetic patients based on explainable artificial intelligence,.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends A feasibility study of diabetic retinopathy detection in type ii diabetic patients based on explainable artificial intelligence,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends A unified approach to interpreting model predictions,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Towards explainable deep neural networks for the automatic detection of diabetic retinopathy,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Very Deep Convolutional Networks for Large-Scale Image Recognition

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Deep residual learning for image recognition,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Densely connected convolutional networks,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Grad-cam: Visual explanations from deep networks via gradient-based localization,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Visual explana- tions for the detection of diabetic retinopathy from retinal fundus images,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Learning robust representation for joint grading of ophthalmic diseases via adaptive curriculum and feature disentanglement,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Insightr-net: Interpretable neural network for regression using similarity-based comparisons to proto- typical examples,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Eye tracking based deep learning analysis for the early detection of diabetic retinopathy: A pilot study,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Explainable artificial in- telligence enabled teleophthalmology for diabetic retinopathy grad- ing and classification,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends The diaretdb1 diabetic retinopathy database and evaluation protocol,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Retinopathy on- line challenge: Automatic detection of microaneurysms in digital color fundus photographs,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy,

Reference 79

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This paper cites Team, RC-RGB-MA: RetinaCheck RGB Microaneurysm dataset , http://www.retinacheck.org/datasets, [Online], 2016.

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Team, RC-RGB-MA: RetinaCheck RGB Microaneurysm dataset , http://www.retinacheck.org/datasets, [Online], 2016

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Porwal, S

Reference 81

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening,

Reference 82

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Reference 83

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Deepdrid: Diabetic retinopa- thy—grading and image quality estimation challenge,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Explainable framework for glaucoma diagnosis by image processing and convolutional neural network synergy: Analysis with doctor evaluation,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Explainable machine learning model for glaucoma diagnosis and its interpretation,

Reference 87

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Support-vector networks,

Reference 88

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Cristianini and J

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Unresolved cited work

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Random forests,

Reference 92

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Explaining the rationale of deep learning glaucoma decisions with adversarial examples,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Deep learning on fundus images detects glaucoma beyond the optic disc,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Explainable ai for glaucoma prediction analysis to understand risk 18 factors in treatment planning,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Shalev-Shwartz and S

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Glaucoma detection and feature visualization from oct images using deep learning,

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

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Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends Detecting glaucoma in the ocular hypertension study using deep learning,

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