arXiv AI June 11, 2026 By Nicolas Pinon (MYRIAD), Robin Trombetta (MYRIAD), Carole Lartizien (MYRIAD)

OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection

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.

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 AI.

benchmarks

arXiv AI

Jun 30

Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach

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

rag diffusion benchmarks

arXiv Machine Learning

Jul 27

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection

arXiv:2607. 22212v1 Announce Type: cross Abstract: Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and class distributions are highly imbalanced.

By Alireza Dastmalchi Saei, Shervin Rahimzadeh Arashloo

benchmarks

arXiv AI

Aug 18

Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI

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

benchmarks safety

arXiv Computer Vision

Sep 2

Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection

arXiv:2603.02974v2 Announce Type: replace Abstract: DINO models provide rich patch-level representations that have recently enabled strong performance in unsupervised anomaly detection (UAD). Most ex...

By Ertunc Erdil, Nico Schulthess, Guney Tombak, Ender Konukoglu

rag benchmarks

arXiv AI

Jun 15

Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning

arXiv:2605. 24609v2 Announce Type: replace-cross Abstract: Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data that may introduce unexpected behavior in clinical tasks.

By Mustafa Kadhim, Viktor Rogowski, Emilia Persson, Camila Gonzalez, Andr\'e Haraldsson, Sofie Ceberg, Mikael Nilsson, Malin K\"ugele, Sven B\"ack, Christian Jamtheim Gustafsson

safety

arXiv AI

Jul 24

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

arXiv:2607. 20530v1 Announce Type: cross Abstract: Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance.

By L\'ea Billet (LAAS, INSA Toulouse, ANITI), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Elodie Chanthery (LAAS), Alexandre Gaffet

diffusion benchmarks

One email a morning, machine-written

Related stories

Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection

Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI

Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection

Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning

CLOE: Christoffel Loss Autoencoder for Anomaly Detection