The Dice similarity coefficient, also known as the Sørensen–Dice index or simply Dice coefficient, is a statistical tool which measures the similarity between two sets of data. This index has become arguably the most broadly used tool in the validation of image segmentation algorithms created with AI, but it is a much more general concept which can be applied sets of data for a variety of applications including NLP.
The equation for this concept is:
2 * |X| ∩ |Y| / (|X| + |Y|)
- where X and Y are two sets
- a set with vertical bars either side refers to the cardinality of the set, i.e. the number of elements in that set, e.g. |X| means the number of elements in set X
- ∩ is used to represent the intersection of two sets, and means the elements that are common to both sets
- 1. Anthony D. Yao , Derrick L. Cheng, Ian Pan, Felipe Kitamura; Deep Learning in Neuroradiology: A Systematic Review of Current Algorithms and Approaches for the New Wave of Imaging Technology; Radiology: Artificial Intelligence. Published Online:Mar 4 2020 doi.org/10.1148/ryai.2020190026