Deep learning is a promising avenue to automate tedious analysis tasks in biomedical imaging. However, its application in such a context is limited by the large amount of labeled data required to train deep learning models. While active learning may be used to reduce the amount of labeling data, many approaches do not consider the cost of annotating, which is often significant in a biomedical imaging setting. In this work we show how annotation cost can be considered and learned during active learning on a classification task on the MNIST dataset.

With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often difficult for non-expert users. In this work, we tackle the problem of learning the quality function of super-resolution images from scores provided by experts. More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input. We conduct a user study in order to evaluate the quality of the predictions of the neural network against those of a human expert. Results show the potential while highlighting some of the limits of the proposed approach.