What is Wrong with Continual Learning in Medical Image Segmentation?

Continual learning protocols are attracting increasing attention from the medical imaging community. In a continual setup, data from different sources arrives sequentially and each batch is only available for a limited period. Given the inherent privacy risks associated with medical data, this setup reflects the reality of deployment for deep learning diagnostic radiology systems. Many techniques exist to learn continuously for classification tasks, and several have been adapted to semantic segmentation. Yet most have at least one of the following flaws: a) they rely too heavily on domain identity information during inference, or b) data as seen in early training stages does not profit from training with later data. In this work, we propose an evaluation framework that addresses both concerns, and introduce a fair multi-model benchmark. We show that the benchmark outperforms two popular continual learning methods for the task of T2-weighted MR prostate segmentation.