What are the different loss functions used in image segmentation?

What are the different loss functions used in image segmentation?

In image segmentation, common loss functions include Cross-Entropy Loss, Dice Loss, and Jaccard Loss. Each has its strengths.

Can you explain how Cross-Entropy Loss works?

Cross-Entropy Loss measures the difference between the predicted probability distribution and the actual distribution. It's effective for multi-class segmentation.

What about Dice Loss? How is it different?

Dice Loss focuses on the overlap between predicted and actual segments, making it particularly useful for imbalanced classes.

When should I use Jaccard Loss instead?

Jaccard Loss is beneficial when you want to emphasize the intersection over the union of predicted and actual segments, especially in cases of high class imbalance.