Hmm it’s difficult to say. In theory cross entropy should has better gradient properties. You can find a post comparing both here https://becominghuman.ai/investigating-focal-and-dice-loss-for-the-kaggle-2018-data-science-bowl-65fb9af4f36c 13.
Anyway could you paste couple of loss plots?. If you report small gradients at an early stage  I assume there exist a smooth plateau in you loss for a high loss value. If you had big gradients but no convergence you would have oscillating loss and probably it’s not the case. Have you tried different set of initializations/lr schemes?