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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 18

Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation

arXiv:2608. 14766v1 Announce Type: cross Abstract: Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity.

By Simon Baur, Arne Schernich, Ekin B\"oke, Wojciech Samek, Jackie Ma

computer-vision safety

arXiv Computer Vision

3d ago

Rethinking Uncertainty Quantification and Entanglement in Image Segmentation

arXiv:2603.18792v3 Announce Type: replace Abstract: Uncertainty quantification (UQ) is crucial in safety-critical applications such as medical image segmentation. Total uncertainty is typically decom...

By Jakob L{\o}nborg Christensen, Vedrana Andersen Dahl, Morten Rieger Hannemose, Anders Bjorholm Dahl, Christian F. Baumgartner

diffusion computer-vision safety

arXiv Machine Learning

Jul 28

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

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.

By Pranav Kaliaperumal, Manisha Kaliaperumal

computer-vision multimodal benchmarks safety

arXiv Machine Learning

Jul 7

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.

By Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand Braeckeveldt

benchmarks safety

arXiv Computer Vision

Aug 31

DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation

arXiv:2604.12411v2 Announce Type: replace Abstract: Segmentation models based on deep neural networks demonstrate strong generalization for medical image segmentation. However, they often exhibit ove...

By Qiuyu Tian, Haoliang Sun, Yunshan Wang, Yinghuan Shi, Yilong Yin

computer-vision

arXiv Computer Vision

Sep 1

Text-Guided Diffusion-Based Adversarial Attacks on Chest X-Ray Images

arXiv:2608.29456v1 Announce Type: new Abstract: As artificial intelligence is increasingly integrated into chest X-ray (CXR) interpretation, triage, and clinical decision support, understanding its v...

By Basudha Pal, Arjun Narayanan, Neha Ajith, Vikas R Bhat, Muhammad Umair

diffusion robotics safety

One email a morning, machine-written

Related stories

Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation

Rethinking Uncertainty Quantification and Entanglement in Image Segmentation

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

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation

Text-Guided Diffusion-Based Adversarial Attacks on Chest X-Ray Images