Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees

Joaquin Alvarez and Edgar Roman-Rangel ()
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Joaquin Alvarez: Department of Computer Science, Instituto Tecnológico Autónomo de México, Mexico City 01080, Mexico
Edgar Roman-Rangel: Department of Computer Science, Instituto Tecnológico Autónomo de México, Mexico City 01080, Mexico

Mathematics, 2025, vol. 13, issue 11, 1-22

Abstract: In this work, we introduce a framework to combine arbitrary image segmentation algorithms from different agents under data privacy constraints to produce an aggregated prediction set satisfying finite-sample risk control guarantees. We leverage distribution-free uncertainty quantification techniques in order to aggregate deep neural networks for image segmentation tasks. Our method can be applied in settings to merge the predictions of multiple agents with arbitrarily dependent prediction sets. Moreover, we perform experiments in medical imaging tasks to illustrate our proposed framework. Our results show that the framework reduced the empirical false positive rate by 50% without compromising the false negative rate, with respect to the false positive rate of any of the constituent models in the aggregated prediction algorithm.

Keywords: risk control; guarantees; distribution-free; uncertainty quantification; ensemble learning; polyps; brain tumors; semantic segmentation; convolutional neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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