  Deep learning approaches achieve state-of-the-art performance for classifying
radiology images, but rely on large labelled datasets that require
resource-intensive annotation by specialists. Both semi-supervised learning and
active learning can be utilised to mitigate this annotation burden. However,
there is limited work on combining the advantages of semi-supervised and active
learning approaches for multi-label medical image classification. Here, we
introduce a novel Consistency-based Semi-supervised Evidential Active Learning
framework (CSEAL). Specifically, we leverage predictive uncertainty based on
theories of evidence and subjective logic to develop an end-to-end integrated
approach that combines consistency-based semi-supervised learning with
uncertainty-based active learning. We apply our approach to enhance four
leading consistency-based semi-supervised learning methods: Pseudo-labelling,
Virtual Adversarial Training, Mean Teacher and NoTeacher. Extensive evaluations
on multi-label Chest X-Ray classification tasks demonstrate that CSEAL achieves
substantive performance improvements over two leading semi-supervised active
learning baselines. Further, a class-wise breakdown of results shows that our
approach can substantially improve accuracy on rarer abnormalities with fewer
labelled samples.
