  In recent years, deep learning (DL) techniques have provided state-of-the-art
performance on different medical imaging tasks. However, the availability of
good quality annotated medical data is very challenging due to involved time
constraints and the availability of expert annotators, e.g., radiologists. In
addition, DL is data-hungry and their training requires extensive computational
resources. Another problem with DL is their black-box nature and lack of
transparency on its inner working which inhibits causal understanding and
reasoning. In this paper, we jointly address these challenges by proposing a
hybrid model, which uses a Bayesian convolutional neural network (BCNN) for
uncertainty quantification, and an active learning approach for annotating the
unlabelled data. The BCNN is used as a feature descriptor and these features
are then used for training a model, in an active learning setting. We evaluate
the proposed framework for diabetic retinopathy classification problem and have
achieved state-of-the-art performance in terms of different metrics.

Machine Learning