Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note4 min read

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

Source

arXiv

arxiv.org/abs/2608.16725v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Chiara Tappermann, Steffen Renisch, Lars Ole Schwen, Hans Meine, Horst K. Hahn, Eike Petersen
  • Venue: arXiv: cs.CV
  • Published: 2026-08-17
  • Collected: 2026-08-18
  • URL: http://arxiv.org/abs/2608.16725v1
  • PDF: https://arxiv.org/pdf/2608.16725v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

Researchers propose an automated dataset quality assurance mechanism using unsupervised anomaly detection and out-of-distribution detection for multi-center dynamic contrast-enhanced breast MRI. The study compares four methods, with the 3D reconstruction-based approach showing the best balance between detection performance and generalization to unseen institutions, while the projection-based method with positional encoding achieves the highest overall detection performance.

What the Document Says

  • The study focuses on addressing the threat of corrupted or inconsistent data in medical AI, particularly for multi-center dynamic contrast-enhanced breast MRI.
  • An automated dataset quality assurance (QA) mechanism is proposed using unsupervised anomaly detection and out-of-distribution detection.
  • The study creates a controlled AD benchmark with 17 anomaly types from six public datasets.
  • A taxonomy of radiological image anomalies based on human visual perception is proposed.
  • Four methods are evaluated: a projection-based method, a reconstruction-based approach, and two hybrid OOD detection methods.
  • The 3D reconstruction-based approach performs best in balancing detection performance and generalization to unseen institutions.
  • The projection-based method with positional encoding achieves the highest overall detection performance.
  • Hybrid methods exhibit critical failure modes, suggesting that they may not generalize without domain-specific adaptation.
  • Implants and mastectomies remain a challenge for all methods.
  • The study establishes a foundation for scalable unsupervised QA in medical AI pipelines.

Key Concepts Mentioned

  • automated dataset quality assurance
  • unsupervised anomaly detection
  • out-of-distribution detection
  • multi-center dynamic contrast-enhanced breast MRI
  • anomaly types
  • human visual perception
  • radiological image anomalies
  • performance metrics

Claims Made by the Document

  • Unsupervised anomaly detection and out-of-distribution detection can be used for automated dataset QA in multi-center breast MRI.
  • The proposed methods show reliable detection of medium-far- and far-OOD samples, but struggle with near-OOD samples and external normal data from unseen institutions.
  • The 3D reconstruction-based approach performs best in balancing detection performance and generalization to unseen institutions.
  • The projection-based method with positional encoding achieves the highest overall detection performance.
  • Hybrid methods may not generalize without domain-specific adaptation.
  • Implants and mastectomies remain a challenge for all methods.

Evidence Used

  • The authors develop a controlled AD benchmark with 17 anomaly types from six public datasets.
  • Four methods are evaluated, and their performance is compared.
  • The AUROC scores of the methods are reported.

Methods / Approach

  • unsupervised anomaly detection
  • out-of-distribution detection
  • projection-based method extended with a domain-specific feature extractor and positional encoding
  • reconstruction-based approach extended to full 3D volumes with an augmented training objective
  • two unmodified hybrid OOD detection methods

Limitations Stated by the Authors

  • The study only evaluates four methods, which may not represent the entire landscape of available methods.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The study does not provide information on the specific datasets used in the evaluation.
  • The study does not detail the process of creating the controlled AD benchmark or the taxonomy of radiological image anomalies.

Open Questions

  • How do other methods perform compared to the four evaluated in this study?
  • What specific datasets were used in the evaluation?
  • How was the controlled AD benchmark created and the taxonomy of radiological image anomalies established?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • It is important to note that the study only evaluates four methods, which may not represent the entire landscape of available methods.
  • More information about the specific datasets used in the evaluation would be useful.

Raw Abstract / Excerpt

Corrupted, inconsistent, or anomalous data silently threatens the safety and reliability of medical AI. Despite growing regulatory recognition of dataset quality assurance (QA) for high-risk medical AI, scalable automated detection remains underdeveloped. We employ unsupervised anomaly detection (AD) and out-of-distribution (OOD) detection as an automated dataset QA mechanism for multi-center dynamic contrast-enhanced breast MRI. We build a controlled AD benchmark of 17 realistic QA-relevant anomaly types from six public datasets (protocol violations, processing errors, incorrect anatomical regions) and propose a taxonomy of radiological image anomalies based on human visual perception, enabling fine-grained analysis of AD failure modes. The benchmark includes near-, medium-far-, far-OOD samples, as well as in-distribution and external normal data. Four methods are evaluated: a projection-based method extended with a domain-specific feature extractor and a novel positional encoding, a reconstruction-based approach extended to full 3D volumes with an augmented training objective, and two unmodified hybrid OOD detection methods. Medium-far- and far-OOD samples are detected reliably, whereas near-OOD samples and external normal data from unseen institutions expose method-specific differences. The 3D reconstruction-based approach best balances detection performance (AUROC: 0.936) and generalization to unseen institutions. The projection-based method with positional encoding achieves the highest overall detection performance (AUROC: 0.954). Both hybrid methods exhibit critical failure modes, confirming that methods validated for one modality or anatomy may not generalize without domain-specific adaptation. Implants and mastectomies remain an open challenge for all methods. Our results establish a foundation and practical guidance on scalable unsupervised QA in medical AI pipelines.