The practice of image recognition can aid this detection by recognizing Diabetic Retinopathy patterns and comparing it with the patient's retina in diagnosis.
Label shift refers to the phenomenon where the marginal probability p(y) of observing a particular class changes between the training and test distributions, while the conditional probability p(x|y) stays fixed.
To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine.
Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space.
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).
In this report, we applied integrated gradients to explaining a neural network for diabetic retinopathy detection.
We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions.
Our experimental results show that neural networks in combination with preprocessing on the images can boost the classification accuracy on this dataset.
In recent years, quadratic weighted kappa has been growing in popularity in the machine learning community as an evaluation metric in domains where the target labels to be predicted are drawn from integer ratings, usually obtained from human experts.