Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees
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...
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/11/1711 |
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| author | Joaquin Alvarez Edgar Roman-Rangel |
| author_facet | Joaquin Alvarez Edgar Roman-Rangel |
| author_sort | Joaquin Alvarez |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-93a3d7f4e6c04d2988993cb78deb7661 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-93a3d7f4e6c04d2988993cb78deb76612025-08-20T03:46:46ZengMDPI AGMathematics2227-73902025-05-011311171110.3390/math13111711Aggregating Image Segmentation Predictions with Probabilistic Risk Control GuaranteesJoaquin Alvarez0Edgar Roman-Rangel1Department of Computer Science, Instituto Tecnológico Autónomo de México, Mexico City 01080, MexicoDepartment of Computer Science, Instituto Tecnológico Autónomo de México, Mexico City 01080, MexicoIn 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.https://www.mdpi.com/2227-7390/13/11/1711risk controlguaranteesdistribution-freeuncertainty quantificationensemble learningpolyps |
| spellingShingle | Joaquin Alvarez Edgar Roman-Rangel Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees Mathematics risk control guarantees distribution-free uncertainty quantification ensemble learning polyps |
| title | Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees |
| title_full | Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees |
| title_fullStr | Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees |
| title_full_unstemmed | Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees |
| title_short | Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees |
| title_sort | aggregating image segmentation predictions with probabilistic risk control guarantees |
| topic | risk control guarantees distribution-free uncertainty quantification ensemble learning polyps |
| url | https://www.mdpi.com/2227-7390/13/11/1711 |
| work_keys_str_mv | AT joaquinalvarez aggregatingimagesegmentationpredictionswithprobabilisticriskcontrolguarantees AT edgarromanrangel aggregatingimagesegmentationpredictionswithprobabilisticriskcontrolguarantees |