Hey guys, I found a way to implement multi-class dice loss, I get satisfying segmentations now. I implemented the loss as explained inref: this paper describes the Tversky loss, a generalised form of dice loss,
Balanced Dice Loss. The Dice Coefficient (DICE), also called the overlap index, is a commonly used metric in validating medical image segmentation. For the binary ground truth images of each class, DICE can be written as: DICEt = 2 N i=1 pitgit + N i=1 pit + N i=1 git + . (...
Balanced Dice Loss. The Dice Coefficient (DICE), also called the overlap index, is a commonly used metric in validating medical image segmentation. For the binary ground truth images of each class, DICE can be written as: DICEt = 2 N i=1 pitgit + N i=1 pit + N i=1 git + . (...