Ground truth

Last revised by Dimitrios Toumpanakis on 18 Apr 2021

Ground truth is a term used in statistics and machine learning to refer to data assumed to be correct.

Regarding the development of machine learning algorithms in radiology, the ground truth for image labeling is sometimes based on pathology or lab results while, in other cases, on the expert opinion of experienced radiologists. In the computing informatics literature the term ground truth can be sometimes used to imply any data presumed to be correctly labeled, and thus it is possible to create synthetic ground truth 3,4.

When developing a machine learning model using ground truth data, it is important to first explore/analyze "how true" the ground truth actually is, i.e. to compare the inter- and intra-observer variability in the data used as ground truth.

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