mlsanityData to 7 Oct 2026

Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI

arXiv 2109.06023 ↗First posted 13 Sep 2021eess.IVcited by 23

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Abstract (arXiv)

In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images. Our work challenges the effectiveness of current Machine Learning (ML) approaches in this application domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR) MR scans provides better anomaly segmentation maps than several different ML-based anomaly detection models. Specifically, our method achieves better Dice similarity coefficients and Precision-Recall curves than the competitors on various popular evaluation data sets for the segmentation of tumors and multiple sclerosis lesions.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 9; 2024: 4; 2025: 2; 2026: 1.

23 papers.

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  1. 2022Diffusion Models for Medical Anomaly Detectioncited by 231
  2. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  3. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRIcited by 45
  4. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  5. 2023Unsupervised Pathology Detection: A Deep Dive Into the State of the Artcited by 24
  6. 2023The role of noise in denoising models for anomaly detection in medical imagescited by 23
  7. 2022Unsupervised Anomaly Localization with Structural Feature-Autoencoderscited by 22
  8. 2022Constrained unsupervised anomaly segmentationcited by 19
  9. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIscited by 18
  10. 2023Many tasks make light work: Learning to localise medical anomalies from multiple synthetic taskscited by 12
  11. 2022On the Pitfalls of Using the Residual Error as Anomaly Scorecited by 11
  12. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
11 more
  1. 2024Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detectioncited by 7
  2. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusioncited by 4
  3. 2023CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localizationcited by 3
  4. 2023AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detectioncited by 3
  5. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  6. 2022AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentationcited by 1
  7. 2022Progressive GANomaly: Anomaly detection with progressively growing GANscited by 1
  8. 2025Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detectioncited by 1
  9. 2025OCSVM-Guided Representation Learning for Unsupervised Anomaly Detectioncited by 1
  10. 2024Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspectivecited by 0
  11. 2026MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRIcited by 0