arXiv:2608. 16725v1 Announce Type: cross Abstract: Corrupted, inconsistent, or anomalous data silently threatens the safety and reliability of medical AI.
By Chiara Tappermann, Steffen Renisch, Lars Ole Schwen, Hans Meine, Horst K. Hahn, Eike Petersen
arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.
By Pritam Kar, Gouri Lakshmi S, Saptarshi Bej
arXiv:2507. 21164v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not available.
By Nicolas Pinon (MYRIAD), Robin Trombetta (MYRIAD), Carole Lartizien (MYRIAD)
arXiv:2606. 07381v1 Announce Type: cross Abstract: Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which are difficult to acquire.
By Prabhjot Kaur, Hakim Ouaalam, Sedat Kandemirli, Sanjay P. Prabhu, Simon K. Warfield
Medical vision-language models typically generate diagnoses through single-pass inference without indicating which image regions support their conclusions. This lack of spatial grounding limits clinical utility: outputs cannot be audited, and models may hallucinate findings on normal scans.
arXiv:2607. 18882v1 Announce Type: cross Abstract: Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features.
By Rick Wilming, Irem Ozseker, Luca Matteo Cornils, Ahc\`ene Boubekki, Benedict Clark, Danny Panknin, Stefan Haufe