Realistic Evaluation of FixMatch on Imbalanced Medical Image Classification Tasks

Maximilian Zenk, David Zimmerer, Fabian Isensee, Paul F. Jäger, Jakob Wasserthal, Klaus H. Maier-Hein

Abstract: Semi-supervised learning offers great potential for medical image analysis, as it reduces the annotation burden for clinicians. In this work, we apply the state-of-the-art method FixMatch to chest X-ray and retinal image datasets. Our comparison with the supervised-only method is based on a fair hyperparameter tuning budget and includes label imbalance in the labeled set, thus simulating a practical evaluation setup. We find that unlabeled data can be used effectively for the retinal images, especially when using additional methods to counteract label imbalance in the unsupervised loss. In experiments with CheXpert, however, FixMatch does not provide substantial gains.