Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process
Fig 2
Overview of the confidence-based workflow.
By only considering labels over a certain probability threshold, we increase the final accuracy of the model at the cost of coverage on the overall dataset (red: wrong label, green: true label, gray: rejected label).