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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.
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.
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.
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.
Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India.
TLDR
This study shows that the automated DR system generalizes to this population of Indian patients in a prospective setting and demonstrates the feasibility of using an automated DR grading system to expand screening programs.
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 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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