Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

Automatic segmentation methods are an important advancement in medical image analysis. We compare our loss function performance against six Dice or cross entropy-based loss functions, across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating that our proposed loss function is robust to class imbalance and consistently outperforms the other loss functions.

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• 1 Machine learning techniques, and deep neural networks in particular, are the advanced for most medical image segmentation tasks.
• 2 Issues with class imbalance pose a significant challenge in medical datasets, with lesions often occupying a considerably smaller volume relative to the background.
• 3 Loss functions used in the training of deep learning algorithms differ in their robustness to class imbalance, with direct consequences for model convergence.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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