  Segmentation has become a crucial pre-processing step to many refined
downstream tasks, and particularly so in the medical domain. Even with recent
improvements in segmentation models, many segmentation tasks remain difficult.
When multiple organs are segmented simultaneously, difficulties are due not
only to the limited availability of labelled data, but also to class imbalance.
In this work we propose dynamic class-based loss strategies to mitigate the
effects of highly imbalanced training data. We show how our approach improves
segmentation performance on a challenging Multi-Class 3D Abdominal Organ
dataset.
