  We introduce unORANIC, an unsupervised approach that uses an adapted loss
function to drive the orthogonalization of anatomy and image-characteristic
features. The method is versatile for diverse modalities and tasks, as it does
not require domain knowledge, paired data samples, or labels. During test time
unORANIC is applied to potentially corrupted images, orthogonalizing their
anatomy and characteristic components, to subsequently reconstruct
corruption-free images, showing their domain-invariant anatomy only. This
feature orthogonalization further improves generalization and robustness
against corruptions. We confirm this qualitatively and quantitatively on 5
distinct datasets by assessing unORANIC's classification accuracy, corruption
detection and revision capabilities. Our approach shows promise for enhancing
the generalizability and robustness of practical applications in medical image
analysis. The source code is available at
https://github.com/sdoerrich97/unORANIC.
