mlsanityData to 14 Oct 2026

Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models

arXiv 2408.03654 ↗First posted 7 Aug 2024eess.IV cs.CVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Congenital malformations of the brain are among the most common fetal abnormalities that impact fetal development. Previous anomaly detection methods on ultrasound images are based on supervised learning, rely on manual annotations, and risk missing underrepresented categories. In this work, we frame fetal brain anomaly detection as an unsupervised task using diffusion models. To this end, we employ an inpainting-based Noise Agnostic Anomaly Detection approach that identifies the abnormality using diffusion-reconstructed fetal brain images from multiple noise levels. Our approach only requires normal fetal brain ultrasound images for training, addressing the limited availability of abnormal data. Our experiments on a real-world clinical dataset show the potential of using unsupervised methods for fetal brain anomaly detection. Additionally, we comprehensively evaluate how different noise types affect diffusion models in the fetal anomaly detection domain.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 21 of its 32 references that are in the map. First posted on arXiv 7 Aug 2024.

Builds on

21 of 32 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusion4 Dec
  2. 2023DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection26 Nov
  3. 2023Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos1 Nov
  4. 2023The role of noise in denoising models for anomaly detection in medical images19 Jan
  5. 2022Denoising diffusion models for out-of-distribution detection14 Nov
  6. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  4. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  5. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 453
  6. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  7. 2016SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasoundcited by 71
  8. 2018Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networkscited by 16
  9. 2022Adversarial vulnerability of powerful near out-of-distribution detectioncited by 16
  10. 2021FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurementscited by 13
  11. 2024Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessmentcited by 13
  12. 2022DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentationcited by 12
3 more
  1. 2023Towards Realistic Ultrasound Fetal Brain Imaging Synthesiscited by 5
  2. 2024Learning semantic image quality for fetal ultrasound from noisy ranking annotationcited by 4
  3. 2022Explainable fetal ultrasound quality assessment with progressive concept bottleneck modelscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound
  2. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
  3. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection
  4. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
  5. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI
  6. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
  7. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
  8. 2025Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models