arXiv Machine Learning July 28, 2026 By Pranav Kaliaperumal, Manisha Kaliaperumal

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation

Read the original on arXiv Machine Learning →

arXiv:2607. 22727v1 Announce Type: cross Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning.

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

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