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

Pulkit Khandelwal

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.