arXiv Machine Learning June 18, 2026 By Hana Jebril, Thomas Pinetz, G\"unter Klambauer, Hrvoje Bogunovi\'c

Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation

Read the original on arXiv Machine Learning →

arXiv:2606. 18860v1 Announce Type: cross Abstract: Reliable pixel-level uncertainty quantification holds the potential to transform clinical workflows by enabling high-fidelity longitudinal monitoring and distinguishing true pathological changes from artifacts.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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