  Deep learning (DL) based diagnostics systems can provide accurate and robust
quantitative analysis in digital pathology. These algorithms require large
amounts of annotated training data which is impractical in pathology due to the
high resolution of histopathological images. Hence, self-supervised methods
have been proposed to learn features using ad-hoc pretext tasks. The
self-supervised training process uses a large unlabeled dataset which makes the
learning process time consuming. In this work, we propose a new method for
actively sampling informative members from the training set using a small proxy
network, decreasing sample requirement by 93% and training time by 62% while
maintaining the same performance of the traditional self-supervised learning
method. The code is available on https://github.com/Reasat/data_efficient_cl
