Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

By Varun Gulshan, Lily H. Peng, Marc Coram, Martin C. Stumpe, Derek J. Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, T. Madams, Jorge A Cuadros and 5 more. 2016

6,915 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging, Medical Imaging

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Citations in the corpus

Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, background): “Deep learning models have shown great promise in the ophthalmological domain (Gulshan et al., 2016; Ting et al., 2017).”
Contrastive Learning of Medical Visual Representations from Paired Images and Text (2020, methodology): “Medical image understanding has the potential to transform healthcare and has seen rapid progress with deep learning (Gulshan et al., 2016; Esteva et al., 2017; De Fauw et al., 2018; Rajpurkar et al., 2018b).”
LAION-5B: An open large-scale dataset for training next generation image-text models (2022, background): “[22] Varun Gulshan, Lily Peng, Marc Coram, Martin C.”
Axiomatic Attribution for Deep Networks (2017, background): “The evaluated integrated gradients as part of wider study with retina specilists and found that integrated gradients help the specialist conﬁrm the predicted DR grade on 28 out of 33 images from the EyePACs-1 dataset (V et al., 2016) chosen for diversity.”
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks (2019, background): “The latter property is particularly important for classification tasks in the medical [20, 18] domain, where fine structures (e.”
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning (2018, background): “Deep learning is ubiquitous: deep neural networks achieve exceptional performance on challenging tasks like machine translation [1], [2], diagnosing medical conditions such as diabetic retinopathy [3], [4] or pneumonia [5], malware detection [6], [7], [8], and classification of images [9], [10].”
A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy (2020, background): “Our team has developed a deep learning algorithm that can provide an assessment of diabetic retinopathy, bypassing the need to wait weeks for an ophthalmologist to review the retinal images [20].”
An overview of deep learning in medical imaging focusing on MRI (2018, background): “l strong initiatives across academia, and multiple large companies are pursuing healthcare projects based on machine learning. Not only medical technology companies, but also for example Google Brain [29, 30, 31]5, DeepMind [32]6, Microsoft [33, 34]7 and IBM [35]8. There is also a plethora of small ”
Opportunities and obstacles for deep learning in biology and medicine (2017, background): “In medical imaging, diabetic retinopathy [46], diabetic macular edema [46], tuberculosis [55], and skin lesion [4] classifiers are highly accurate and comparable to clinician performance.”
A survey on deep learning in medical image analysis (2017, methodology): “Gülsün et al. (2016) CT CNN Coronary centerline extraction; CNN classifies paths as correct or leakages”
Self-Normalizing Neural Networks (2017, background): “At vision challenges, CNNs are constantly winning, for example at the large ImageNet competition [19, 13], but also almost all Kaggle vision challenges, such as the “Diabetic Retinopathy” and the “Right Whale” challenges [7, 12].”
Detecting Cancer Metastases on Gigapixel Pathology Images (2017, background): “, [24,9]) has shown that pre-training on a different domain improves performance.”
Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, background): “Fueled by the massive inﬂux of data, sophisticated algorithms and extensive computational resources, modern machine learning has found surprising applications in such diverse domains as medical diagnosis [12], [39], facial recognition [35] and credit risk assessment [2].”
Scalable and accurate deep learning with electronic health records (2018, background): “Deep learning emerged as the preferred machine learning approach in machine perception problems ranging from computer vision to speech recognition, but has more recently proven useful in natural language processing, sequence prediction, and mixed modality data settings([36, 16, 21, 56]).”