  Deep learning models have been successfully used in medical image analysis
problems but they require a large amount of labeled images to obtain good
performance.Deep learning models have been successfully used in medical image
analysis problems but they require a large amount of labeled images to obtain
good performance. However, such large labeled datasets are costly to acquire.
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 then extend our sampling method to define a better initial training
set, without the need for a trained model, by using ORB feature descriptors. We
validate MedAL on 3 medical image datasets and show that our method is robust
to different dataset properties. MedAL is also efficient, achieving 80%
accuracy on the task of Diabetic Retinopathy detection using only 425 labeled
images, corresponding to a 32% reduction in the number of required labeled
examples compared to the standard uncertainty sampling technique, and a 40%
reduction compared to random sampling.
