Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
This work addresses the challenge of limited labeled data in medical imaging for clinicians and researchers, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of diagnosing ophthalmological conditions from OCT images with few labeled examples by applying semi-supervised learning methods MixMatch and FixMatch, finding that they outperformed transfer learning baselines across all labeled data fractions.
Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Despite this, they are currently receiving relatively little attention in medical image analysis literature. Instead, most practitioners and researchers focus on supervised or transfer learning approaches. The recently proposed MixMatch and FixMatch algorithms have demonstrated promising results in extracting useful representations while requiring very few labels. Motivated by these recent successes, we apply MixMatch and FixMatch in an ophthalmological diagnostic setting and investigate how they fare against standard transfer learning. We find that both algorithms outperform the transfer learning baseline on all fractions of labelled data. Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged. Our code is available online: https://github.com/Valentyn1997/oct-diagn-semi-supervised