Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process.

ABSTRACT: The digitization of natural history collections over the past three decades has unlocked a treasure trove of specimen imagery and metadata. There is great interest in making this data more useful by further labeling it with additional trait data, and modern "deep learning" machine learning techniques utilizing convolutional neural nets (CNNs) and similar networks show particular promise to reduce the amount of required manual labeling by human experts, making the process much faster and less expensive. However, in most cases, the accuracy of these approaches is too low for reliable utilization of the automatic labeling, typically in the range of 80-85% accuracy. In this paper, we present and validate an approach that can greatly improve this accuracy, essentially by examining the "confiden

SUBMITTER: Bateux Q 

PROVIDER: S-EPMC12629457 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature