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.

翻译：分割已成为许多精细下游任务的关键预处理步骤，在医学领域尤其如此。尽管分割模型近期有所改进，许多分割任务仍然困难重重。当同时分割多个器官时，困难不仅源于标注数据的有限性，还源于类别不平衡。本研究提出基于动态类别的损失策略，以缓解高度不平衡训练数据的影响。我们展示了该方法在具有挑战性的多类别三维腹部器官数据集上如何提升分割性能。