Training deep convolutional neural network for classification in medical tasks is often difficult due to the lack of annotated data samples. Deep convolutional networks (CNN) has been successfully used as an automatic detection tool to support the grading of diabetic retinopathy and macular edema. Nevertheless, the manual annotation of exudates in eye fundus images used to classify the grade of the DR is very time consuming and repetitive for clinical personnel. Active learning algorithms seek to reduce the labeling effort in training machine learning models. This work presents a label-efficient CNN model using the expected gradient length, an active learning algorithm to select the most informative patches and images, converging earlier and to a better local optimum than the usual SGD (Stochastic Gradient Descent) strategy. Our method also generates useful masks for prediction and segments regions of interest.
