Deep metric learning for few-shot X-ray image classification

Deep learning models have proven the potential to aid professionals with medical image analysis, including many image classification tasks. However, the scarcity of data in medical imaging poses a significant challenge, as the limited availability of diverse and comprehensive datasets hinders the development and evaluation of accurate and robust imaging algorithms and models. Few-shot learning approaches have emerged as a potential solution to address this issue. In this research, we propose to deploy the Generalized Metric Learning Model for Few-Shot X-ray Image Classification. The model comprises a feature extractor to embed images into a lower-dimensional space and a distance-based classifier for label assignment based on the relative distance of these embeddings. We extensively evaluate the model using various pre-trained convolutional neural networks (CNNs) and vision transformers (ViTs) as feature extractors. We also assess the performance of the commonly used distance-based classifiers in several few-shot settings. Finally, we analyze the potential to adapt the feature encoders to the medical domain with both supervised and self-supervised frameworks. Our model achieves 0.689 AUROC in 2-way 5-shot COVID-19 recognition task when combined with REMEDIS (Robust and Efficient Medical Imaging with Self-supervision) domain-adapted model as feature extractor, and 0.802 AUROC in 2-way 5-shot tuberculosis recognition task with domain-adapted DenseNet-121 model. Moreover, the simplicity and flexibility of our approach allows for easy improvement in the feature, either by incorporating other few-shot methods or new, powerful architectures into the pipeline.