arXiv AI June 30, 2026 By Pritam Kar, Gouri Lakshmi S, Saptarshi Bej

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.

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

arXiv AI

Jun 11

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.

By Nicolas Pinon (MYRIAD), Robin Trombetta (MYRIAD), Carole Lartizien (MYRIAD)

benchmarks

arXiv AI

1d ago

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 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 2

Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound

arXiv:2607. 00744v1 Announce Type: cross Abstract: Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management.

By Huanwen Liang, Yuhao Huang, Xiliang Zhu, Yuanji Zhang, Xuedong Deng, Xinru Gao, Guowei Tao, Yuhan Zhang, Dong Ni

fine-tuning

arXiv AI

2d ago

CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification

arXiv:2608. 13939v1 Announce Type: cross Abstract: Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are aggregated into five risk levels (TR1-TR5).

By Bingxin Yu, Xueli Wang, Jerry Zhou, Wenyan Wang, Li Wen, Lan Huang, Xin Feng, Fengfeng Zhou, Kewei Li

rag multimodal safety

arXiv Machine Learning

Jun 16

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

arXiv:2606. 15837v1 Announce Type: cross Abstract: Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols.

By Jimut B. Pal, Suyash P. Awate

computer-vision

Related stories

OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection

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

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

Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound

CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation