Generalization in Cardiac Image Segmentation.

Deep learning methods have achieved great success in medical imaging applications. Although data is very crucial for deep learning models, the medical imaging domain is restricted by the limited size of datasets and differences between them. This makes it difficult for models to generalize across datasets and achieve robust performance in practical settings. Therefore, knowing how to combine different datasets together to take advantage of new data while retaining performance on previous data becomes an important problem. In this paper, we focus on the task of cardiac image semantic segmentation and synthesize five different real-world scenarios to find the optimal training approach for deep learning models to achieve good generalization across different datasets.