Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating
This addresses the annotation efficiency problem for researchers and practitioners in medical imaging and other domains requiring ranking, though it appears incremental as it builds on existing pairwise comparison and sorting techniques.
The paper tackles the problem of high annotation burden in ranking by pairwise comparisons, which scales quadratically with dataset size, by proposing a human-in-the-loop sorting method that reduces the number of comparisons needed for full ordinal ranking without compromising inter-rater reliability, as demonstrated in a medical image quality rating application.
Ranking by pairwise comparisons has shown improved reliability over ordinal classification. However, as the annotations of pairwise comparisons scale quadratically, this becomes less practical when the dataset is large. We propose a method for reducing the number of pairwise comparisons required to rank by a quantitative metric, demonstrating the effectiveness of the approach in ranking medical images by image quality in this proof of concept study. Using the medical image annotation software that we developed, we actively subsample pairwise comparisons using a sorting algorithm with a human rater in the loop. We find that this method substantially reduces the number of comparisons required for a full ordinal ranking without compromising inter-rater reliability when compared to pairwise comparisons without sorting.