These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Average Dice coefficient
The Average Dice coefficient, also known as the Dice Similarity Coefficient, is a commonly used metric in the field of medical image analysis and computer vision. It measures the similarity between two sets of data, such as binary masks or segmentations of an image.
The Dice coefficient is defined as the ratio of the intersection of two sets of data to their union:
Dice coefficient = 2 |A ∩ B| / (|A| + |B|)
, where A and B are the two sets of data being compared, and |A| and |B| represent the number of elements in each set. The Dice coefficient ranges from 0 to 1, with 1 indicating a perfect match between the two sets of data and 0 indicating no overlap.
The Average Dice coefficient is an extension of the Dice coefficient that is calculated over multiple sets of data, rather than just two. It is typically used to evaluate the performance of a segmentation algorithm on a set of images, by comparing the segmentations produced by the algorithm to a set of ground truth segmentations.
In summary, the Average Dice coefficient is a widely used metric in medical image analysis and computer vision, used to quantify the similarity between sets of data, such as binary masks or segmentations of an image.
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