A Comprehensive Overview of Multi Loss Functions (BCE Loss + Focal Loss + Dice Loss)
When it comes to image segmentation tasks, choosing the right loss function plays a pivotal role in the overall performance of machine learning models. In recent years, the Combination of multi loss functions has been proven to be a successful approach to improve the results of image segmentation tasks. This article will give an overview of the Multi Loss (BCE Loss + Focal Loss + Dice Loss) function and how it benefits from all three loss functions to achieve better results.
The Components of the Multi Loss Function: BCE Loss, Focal Loss, and Dice Loss
The proposed loss function consists of three different loss functions: Binary Cross-Entropy (BCE) Loss, Focal Loss, and Dice Loss.
BCE Loss
The Binary Cross-Entropy Loss function calculates probabilities and compares each actual class output with predicted probabilities. It is based on Bernoulli distribution loss and is mostly used when there are only two classes available. In our case, there are exactly two classes available: foreground and background. BCE Loss is used for pixel-level classification, and it is an essential component of our proposed loss function.
Focal Loss
Focal Loss is a variant of BCE Loss that enables the model to focus on learning hard examples by decreasing the weights of easy examples. This loss function works well when the data is highly imbalanced. By giving more weight to hard examples, Focal Loss helps the model to learn more efficiently from challenging data. In our proposed loss function, we use 0.25 as the value for alpha and 2.0 as the value of gamma to optimize the Focal Loss component.
Dice Loss
The Dice Loss function is inspired by the Dice Coefficient Score used to evaluate the results of image segmentation tasks. Dice Coefficient is convex in nature, so it has been adjusted to be more traceable. The Dice Loss function is used to calculate the similarity between two images, and it enables the model to learn better boundary representation. In our proposed loss function, we use Dice Loss to complement BCE Loss and Focal Loss in learning better boundary representation.
How the Components of Multi Loss Function Work Together?
The proposed loss function represents the sum of BCE Loss, Focal Loss, and Dice Loss, combined to benefit from all three loss functions. Firstly, BCE Loss is used for pixel-wise classification, then Focal Loss is used for learning hard examples, and finally, Dice Loss is used for learning better boundary representation. By combining all three losses, the model can learn effectively from different perspectives to achieve better results.
Multi Loss Function is an effective approach that combines different loss functions to improve segmentation tasks' results. The combination of BCE Loss, Focal Loss, and Dice Loss enables the model to learn from different perspectives, ultimately leading to superior results. In particular, BCE Loss is used for pixel-level classification, Focal Loss is used for learning hard examples, and Dice Loss is used for learning better boundary representation. By applying these loss functions in combination, segmentation models can improve their accuracy and deliver outstanding results.
