I am wondering when composite losses such as Dice + Focal Loss, Dice + Cross-Entropy Loss, or Generalized Dice + Focal Loss are preferred to using regular Dice/CE Loss in training image segmentation algorithms.

Under what circumstances can they help the algorithm converge better? And this is a bit of a more general question, but does there exist a rough strategy to determine good compound losses for certain algorithms?