\section{Conclusion}
\label{sec:conclusion}
We propose using a non-parametric-based neighborhood classification method for medical imaging tasks involving distribution shifts as a novel test-time generalization method. By utilizing target information and neighbors in the embedding space, we sequentially adjust the weights of the classifier, providing an efficient yet powerful generalization technique. {A limitation of our method, that is also present for established techniques, is given by the requirement for a shared label space between domains. Moreover, our method relies on prototypes, constructed from the source domain, for target classes separation. In future work, we aim to address classification capabilities for new, unseen categories by application of meta-learning techniques and involvement of additional dataset, for construction of improved prototypes. }Furthermore, we provide additional experiments to demonstrate the method's utility in uncertain scenarios and settings that require limited computational resources. 


\section*{Acknowledgements}
This paper is supported by the DAAD programme Konrad Zuse Schools of Excellence in Artificial Intelligence, sponsored by the Federal Ministry of Education and Research. We thank Lina Felsner for her assistance with the diagrams.
