  In this paper, we propose a novel semi-supervised learning (SSL) framework
named BoostMIS that combines adaptive pseudo labeling and informative active
annotation to unleash the potential of medical image SSL models: (1) BoostMIS
can adaptively leverage the cluster assumption and consistency regularization
of the unlabeled data according to the current learning status. This strategy
can adaptively generate one-hot "hard" labels converted from task model
predictions for better task model training. (2) For the unselected unlabeled
images with low confidence, we introduce an Active learning (AL) algorithm to
find the informative samples as the annotation candidates by exploiting virtual
adversarial perturbation and model's density-aware entropy. These informative
candidates are subsequently fed into the next training cycle for better SSL
label propagation. Notably, the adaptive pseudo-labeling and informative active
annotation form a learning closed-loop that are mutually collaborative to boost
medical image SSL. To verify the effectiveness of the proposed method, we
collected a metastatic epidural spinal cord compression (MESCC) dataset that
aims to optimize MESCC diagnosis and classification for improved specialist
referral and treatment. We conducted an extensive experimental study of
BoostMIS on MESCC and another public dataset COVIDx. The experimental results
verify our framework's effectiveness and generalisability for different medical
image datasets with a significant improvement over various state-of-the-art
methods.
