Method, apparatus, and system for providing quality assurance for training a feature prediction model

An apparatus for providing quality assurance for training a feature prediction model comprising:

• at least one processor;
• and
• at least one memory including computer program code for one or more programs,
• the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,
• train the feature prediction model to label one or more features by using a training data set comprising a plurality of data items with manually marked feature labels;
• process the training data set using the trained feature prediction model to generate automatically marked feature labels for the plurality of data items;
• compute precision data indicating a respective precision between the manually marked feature labels and the automatically marked feature labels for each of the plurality of data items in the training data set;
• and
• initiate a quality assurance procedure on said each of the plurality of data items based on a determination that the precision data does not satisfy a quality assurance criterion.

A non-transitory computer-readable storage medium for providing quality assurance for training a prediction model to label one or more features detected in image data, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:

• training, by a processor, the feature prediction model to label the one or more features by using a training data set comprising a plurality of images with manually marked feature labels;
• processing the training data set using the trained feature prediction model to generate automatically marked feature labels for the plurality of images of the training data set;
• computing precision data indicating a respective precision between the manually marked feature labels and the automatically marked feature labels for each of the plurality of images in the training data set;
• and
• initiating a quality assurance procedure on said each of the plurality of images based on a determination that the precision data does not satisfy a quality assurance criterion.