AI in the Loop -- Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines
This provides a method for safely implementing machine learning in clinical practice by monitoring segmentation pipelines, though it is incremental as it builds on existing ensemble and uncertainty estimation techniques.
The paper tackled the problem of identifying poor-performing automated medical image segmentations by using disagreement between sub-models trained on different dataset folds as a proxy for model confidence, effectively flagging low-performing segmentations in abdominal CT and MR datasets for kidney tumors, with flagged images including smaller tumors in an external dataset.
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.