Balaram, S., Nguyen, C. M., Kassim, A., & Krishnaswamy, P. (2022). Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, 675–685. https://doi.org/10.1007/978-3-031-16431-6_64
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 semisupervised 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.
This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151
Research efforts were supported by the Singapore International Graduate Award (SINGA Award), Agency for Science, Technology and Research (A*STAR) as well as funding and infrastructure for deep learning and medical imaging R&D from the Institute for Infocomm Research, Science and Engineering Research Council, A*STAR.