Keras: Dice Coefficient `Loss` Function Is Negative and Increasing with Epochs

The Dice coefficient, a statistical validation metric, is commonly used in image segmentation tasks. It measures the overlap between two samples and is particularly prevalent in healthcare applications like MRI scans. This article discusses the scenario where the Dice coefficient loss function in a Keras model is observed to be negative and increasing with iterations, presenting both technical insights and practical guidance.

Understanding the Dice Coefficient

The Dice coefficient ranges from 0 to 1, where 1 represents a perfect overlap. It is given by the formula:

$$ $$

Where:

• $A$ is the predicted set of positives,
• $B$ is the actual set of positives.

Dice Coefficient `Loss` Function

For optimization, many frameworks redefine the Dice coefficient as a loss function, aiming for maximization by minimizing the negative Dice score. The loss can be represented as:

$$ $$

Common Issues: Negative and Increasing `Loss`

Potential Causes

1. Implementation Errors: Errors during the coding phase, such as wrong casting to integer types, can inadvertently affect calculations.
2. Data Preprocessing: Incorrect preprocessing steps might lead to incorrect model inputs, causing unexpected parameters in loss calculations.
3. Learning Rate: A high learning rate might lead to the model overshooting the optimal minima, resulting in increased, rather than decreased, loss.
4. Model Architecture: Complex models might have too many parameters, which might lead to overfitting on initial epochs worsened by less regularization.
5. Weight Initialization: Poor weight initialization might lead to a suboptimal loss surface exploration.

Troubleshooting Steps

• Validate inputs and labels after every preprocessing stage.
• Plot loss values over epochs.
• Experiment with different learning rates and use a learning rate scheduler.
• Use simpler models first to ensure the baseline performance before complexity is added.
• Check data distribution and verify it aligns with initial expectations.

Example Scenario

Imagine training a model for tumor segmentation. You might notice:

• `Loss` starts at a negative value: indicating that your model has unexpectedly high overlap initially or might indicate an implementation error.
• `Loss` increases over epochs: suggesting optimization might be diverging.

Example Code in Keras