mlsanityData to 7 Oct 2026

Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound

arXiv 2407.15119 ↗First posted 21 Jul 2024eess.IVcited by 3

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Ultrasonography is an essential tool in mid-pregnancy for assessing fetal development, appreciated for its non-invasive and real-time imaging capabilities. Yet, the interpretation of ultrasound images is often complicated by acoustic shadows, speckle noise, and other artifacts that obscure crucial diagnostic details. To address these challenges, our study presents a novel unsupervised anomaly detection framework specifically designed for fetal ultrasound imaging. This framework incorporates gestational age filtering, precise identification of fetal standard planes, and targeted segmentation of brain regions to enhance diagnostic accuracy. Furthermore, we introduce the use of denoising diffusion probabilistic models in this context, marking a significant innovation in detecting previously unrecognized anomalies. We rigorously evaluated the framework using various diffusion-based anomaly detection methods, noise types, and noise levels. Notably, AutoDDPM emerged as the most effective, achieving an area under the precision-recall curve of 79.8% in detecting anomalies. This advancement holds promise for improving the tools available for nuanced and effective prenatal diagnostics.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 14 of its 27 references that are in the map. First posted on arXiv 21 Jul 2024.

Builds on

14 of 27 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models31 May
  2. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI7 Mar
  3. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 11

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  5. 2019Unsupervised Anomaly Localization using Variational Auto-Encoderscited by 73
  6. 2016SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasoundcited by 71
  7. 2020Unsupervised Lesion Detection via Image Restoration with a Normative Priorcited by 65
  8. 2022A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysiscited by 29
  9. 2018Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networkscited by 16
  10. 2019Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographerscited by 10
  11. 2020Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndromecited by 2

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classificationcited by 3
  2. 2025Detection of Medial Epicondyle Avulsion in Elbow Ultrasound Images via Bone Structure Reconstructioncited by 0
  3. 2025Adaptive Contrast Adjustment Module: A Clinically-Inspired Plug-and-Play Approach for Enhanced Fetal Plane Classificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
  2. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
  3. 2025Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models
  4. 2024Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations
  5. 2025Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
  6. 2024MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRI
  7. 2025FetalFlex: Anatomy-Guided Diffusion Model for Flexible Control on Fetal Ultrasound Image Synthesis
  8. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models