Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging

TL;DR

This paper introduces a meta-learning framework for domain generalization in medical imaging, enabling models to adapt to unseen data distributions with minimal examples, improving robustness across diverse clinical scenarios.

Contribution

It proposes a model-agnostic meta-learning approach for domain generalization in biomedical imaging, capable of rapid adaptation with few-shot learning, independent of model architecture.

Findings

Improved generalization across different medical imaging datasets.

Effective few-shot adaptation to unseen domains.

Potential to enhance clinical deployment of AI models.

Abstract

Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be hindered when there is significant difference in the underlying statistics between the target and source domains. In this work, we adapt a domain generalization method based on a model-agnostic meta-learning framework to biomedical imaging. The method learns a domain-agnostic feature representation to improve generalization of models to the unseen test distribution. The method can be used for any imaging task, as it does not depend on the underlying model architecture. We validate the approach through a computed tomography (CT) vertebrae segmentation task across healthy and pathological cases on three datasets. Next, we employ few-shot learning, i.e. training the generalized model using very few examples from the unseen domain, to quickly adapt the model to new unseen data distribution. Our results suggest that the method could help generalize models across different medical centers, image acquisition protocols, anatomies, different regions in a given scan, healthy and diseased populations across varied imaging modalities.