SPLAL: Similarity-based pseudo-labeling with alignment loss for
  semi-supervised medical image classification
Md Junaid Mahmood Pranaw Raj Divyansh Agarwal Suruchi Kumari Pravendra Singh
Medical image classification is a challenging task due to the scarcity of
labeled samples and class imbalance caused by the high variance in disease
prevalence. Semi-supervised learning (SSL) methods can mitigate these
challenges by leveraging both labeled and unlabeled data. However, SSL methods
for medical image classification need to address two key challenges: (1)
estimating reliable pseudo-labels for the images in the unlabeled dataset and
(2) reducing biases caused by class imbalance. In this paper, we propose a
novel SSL approach, SPLAL, that effectively addresses these challenges. SPLAL
leverages class prototypes and a weighted combination of classifiers to predict
reliable pseudo-labels over a subset of unlabeled images. Additionally, we
introduce alignment loss to mitigate model biases toward majority classes. To
evaluate the performance of our proposed approach, we conduct experiments on
two publicly available medical image classification benchmark datasets: the
skin lesion classification (ISIC 2018) and the blood cell classification
dataset (BCCD). The experimental results empirically demonstrate that our
approach outperforms several state-of-the-art SSL methods over various
evaluation metrics. Specifically, our proposed approach achieves a significant
improvement over the state-of-the-art approach on the ISIC 2018 dataset in both
Accuracy and F1 score, with relative margins of 2.24\% and 11.40\%,
respectively. Finally, we conduct extensive ablation experiments to examine the
contribution of different components of our approach, validating its
effectiveness.