AI in the Loop -- Functionalizing Fold Performance Disagreement to
  Monitor Automated Medical Image Segmentation Pipelines
Harrison C. Gottlich Panagiotis Korfiatis Adriana V. Gregory Timothy L. Kline
Methods for automatically flag poor performing-predictions are essential for
safely implementing machine learning workflows into clinical practice and for
identifying difficult cases during model training. We present a readily
adoptable method using sub-models trained on different dataset folds, where
their disagreement serves as a surrogate for model confidence. Thresholds
informed by human interobserver values were used to determine whether a final
ensemble model prediction would require manual review. In two different
datasets (abdominal CT and MR predicting kidney tumors), our framework
effectively identified low performing automated segmentations. Flagging images
with a minimum Interfold test Dice score below human interobserver variability
maximized the number of flagged images while ensuring maximum ensemble test
Dice. When our internally trained model was applied to an external publicly
available dataset (KiTS21), flagged images included smaller tumors than those
observed in our internally trained dataset, demonstrating the methods
robustness to flagging poor performing out-of-distribution input data.
Comparing interfold sub-model disagreement against human interobserver values
is an efficient way to approximate a model's epistemic uncertainty - its lack
of knowledge due to insufficient relevant training data - a key functionality
for adopting these applications in clinical practice.