Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks

Understand

The Dice score is widely used for binary segmentation due to its robustness to class imbalance.

• Soft generalisations of the Dice score allow it to be used as a loss function for training convolutional neural networks (CNN).
• Although CNNs trained using mean-class Dice score achieve state-of-the-art results on multi-class segmentation, this loss function does neither take advantage of inter-class relationships nor multi-scale information.
• We argue that an improved loss function should balance misclassifications to favour predictions that are semantically meaningful.