What are the best loss functions for image segmentation?

What are the best loss functions for image segmentation?

The most commonly used loss functions for image segmentation include Cross-Entropy Loss, Dice Loss, and Focal Loss. Each has its strengths depending on the specific task.

Can you explain how Dice Loss works?

Sure! Dice Loss measures the overlap between the predicted segmentation and the ground truth. It ranges from 0 to 1, where 1 indicates perfect overlap.

When should I use Focal Loss instead of Cross-Entropy Loss?

Focal Loss is particularly useful when dealing with class imbalance, as it down-weights easy examples and focuses more on hard-to-classify instances.

Are there any scenarios where Cross-Entropy Loss performs better?

Yes, Cross-Entropy Loss is often preferred in cases with balanced classes and when you want a straightforward probabilistic interpretation of the outputs.