What will regularized continuous learning performs if it was used to medical image segmentation: a preliminary analysis

Regularization-based continuous learning (RCL) has been proven to be highly effective on struggling against catastrophic forgetting. However, its application in medical image segmentation (MIS) is relatively scarce. In this paper, we provide a unified description of 6 RCL methods (LwF, LwM, EWC, SI, MAS, and PIGWM) using Taylor expansion and investigate their performances in 2 classic MIS scenarios, namely retinal vessel segmentation (RVS) and cardiac left ventricle segmentation (CLVS) on 8 datasets (CHASE, DRHAGIS, RITE and STARE for the former and M&Ms, LVSC, ACDC and SCD for the latter). We also explore the influence of different task orders (easy to hard or hard to easy), optimizers (Adam or SGD), and parameter capacities (2, 3, 4 or 5 down- and up-sampling pairs) on the performance of these methods. Our experimental results show that these methods are capable of mitigating catastrophic forgetting to a certain extent. Comparing to a hard-to-easy order, most of the methods perform better on all of the already known tasks in an easy-to-hard order. Optimizer Adam performs better on RVS and CLVS. Capacity increases are obviously effective for CLVS, but they have no significant impact on RVS.