How do loss functions affect segmentation performance in AI models?

How do loss functions affect segmentation performance in AI models?

Loss functions are crucial in training AI models for segmentation tasks as they guide the optimization process.

Can you explain the different types of loss functions used in segmentation?

Sure! Common loss functions include Cross-Entropy Loss, Dice Loss, and Focal Loss, each serving different purposes.

What impact does using the wrong loss function have on model performance?

Using an inappropriate loss function can lead to poor convergence, inaccurate segmentations, and overall reduced model performance.

Are there specific loss functions recommended for medical image segmentation?

Yes, Dice Loss and Tversky Loss are often recommended for medical image segmentation due to their sensitivity to class imbalances.