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We have attempted to replicate the main method in 'Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs' published in JAMA 2016; 316(22).
We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection.
Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed.
Our online method enhances performance of its underlying baseline deep network.
In this paper, we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus.
The proposed method is a filter-based feature selection method, which directly utilises the Menger Curvature for ranking all the attributes in the given data set.
Annotated training data insufficiency remains to be one of the challenges of applying deep learning in medical data classification problems.