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The recent achievements of Deep Learning rely on the test data being similarin distribution to the training data. In an ideal case, Deep Learning modelswould achieve Out-of-Distribution (OoD) Generalization, i.e. reliably makepredictions on out-of-distribution data. Yet in practice, models usually failto generalize well when facing a shift in distribution. Several methods werethereby designed to improve the robustness of the features learned by a modelthrough Regularization- or Domain-Prediction-based schemes. Segmenting medicalimages such as MRIs of the hippocampus is essential for the diagnosis andtreatment of neuropsychiatric disorders. But these brain images often sufferfrom distribution shift due to the patient's age and various pathologiesaffecting the shape of the organ. In this work, we evaluate OoD Generalizationsolutions for the problem of hippocampus segmentation in MR data using bothfully- and semi-supervised training. We find that no method performs reliablyin all experiments. Only the V-REx loss stands out as it remains easy to tune,while it outperforms a standard U-Net in most cases.

How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?