Automated identification of diatoms from microscopic images is a cornerstone of large-scale freshwater biomonitoring, but the high diversity of species and strong class imbalance make reliable classification difficult. A new preprint on arXiv explores whether uncertainty estimation can help address these challenges by signaling when automated predictions are likely to be wrong.
The study, titled "On the Benefits of Uncertainty Estimation for Species Classification: A Case Study on Optimizing Expert re-Annotation," focuses on using uncertainty scores to prioritize which images need expert review. Rather than asking experts to re-check all classifications, the method would direct their attention to the most uncertain cases, potentially saving time and improving overall accuracy.
Because the abstract is truncated, specific results are not available in the source. The authors frame the work as a case study in optimizing expert re-annotation, suggesting that uncertainty information can be a practical tool for integrating human oversight into automated biomonitoring pipelines. The approach may be particularly useful for rare or visually similar species, where misclassification is most likely.