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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
TLDR
An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.
Towards better measurement of attention and satisfaction in mobile search
TLDR
This paper studied whether tracking the browser viewport on mobile phones could enable accurate measurement of user attention at scale, and provide good measurement of search satisfaction in the absence of clicks, and found strong correlations between gaze duration and viewport duration on a per result basis.
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
TLDR
Deep learning predicts, from retinal images, cardiovascular risk factors—such as smoking status, blood pressure and age—not previously thought to be present or quantifiable in these images.
Massively Multitask Networks for Drug Discovery
TLDR
The results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process and investigate several aspects of the multitask framework.
Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
TLDR
Adjudication reduces the errors in DR grading by using a small number of adjudicated consensus grades as a tuning dataset and higher-resolution images as input, and to train an improved automated algorithm for DR grading.
A deep learning system for differential diagnosis of skin diseases
TLDR
A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practice.
Scientific Discovery by Generating Counterfactuals using Image Translation
TLDR
This work proposes a framework to convert predictions from explanation techniques to a mechanism of discovery, and shows how generative models in combination with black-box predictors can be used to generate hypotheses that can be critically examined.
Deep learning for predicting refractive error from retinal fundus images
TLDR
The ability to estimate refractive error with high accuracy from retinal fundus photos has not been previously known and demonstrates that deep learning can be applied to make novel predictions from medical images.
Deep learning in ophthalmology: The technical and clinical considerations
TLDR
Global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable are described and the potential challenges for clinical adoption are discussed.
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program
TLDR
Across different severity levels of DR for determining referable disease, deep learning significantly reduced the false negative rate at the cost of slightly higher false positive rates (2%).
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