mlsanityData to 7 Oct 2026

Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment

arXiv 2403.08700 ↗First posted 13 Mar 2024eess.IV cs.CV cs.HCcited by 13

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Abstract (arXiv)

Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, acquiring high-quality standard planes is difficult, influenced by the sonographer's expertise and factors like the maternal BMI or fetus dynamics. In this work, we explore diffusion-based counterfactual explainable AI to generate realistic, high-quality standard planes from low-quality non-standard ones. Through quantitative and qualitative evaluation, we demonstrate the effectiveness of our approach in generating plausible counterfactuals of increased quality. This shows future promise for enhancing training of clinicians by providing visual feedback and potentially improving standard plane quality and acquisition for downstream diagnosis and monitoring.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

19 of 43 references.

References · 19

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  4. 2023Universal Guidance for Diffusion Modelscited by 426
  5. 2023FreeDoM: Training-Free Energy-Guided Conditional Diffusion Modelcited by 290
  6. 2023Manifold Preserving Guided Diffusioncited by 153
  7. 2022Diffusion Models for Counterfactual Explanationscited by 79
  8. 2016SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasoundcited by 71
  9. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  10. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  11. 2023Adversarial Counterfactual Visual Explanationscited by 47
  12. 2023Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generationcited by 41
7 more
  1. 2022DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentationcited by 12
  2. 2023Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videoscited by 12
  3. 2023Towards Realistic Ultrasound Fetal Brain Imaging Synthesiscited by 5
  4. 2024Learning semantic image quality for fetal ultrasound from noisy ranking annotationcited by 4
  5. 2022Multimodal-GuideNet: Gaze-Probe Bidirectional Guidance in Obstetric Ultrasound Scanningcited by 3
  6. 2023Removing confounding information from fetal ultrasound imagescited by 3
  7. 2023Hierarchical Agent-based Reinforcement Learning Framework for Automated Quality Assessment of Fetal Ultrasound Videocited by 3

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Citing papers per year: 2024: 3; 2025: 6; 2026: 4.

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  1. 2024Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencodercited by 10
  2. 2024Counterfactual Explanations via Riemannian Latent Space Traversalcited by 9
  3. 2025Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Freecited by 9
  4. 2025Causally Steered Diffusion for Automated Video Counterfactual Generationcited by 5
  5. 2026Visual Disentangled Diffusion Autoencoders: Scalable Counterfactual Generation for Foundation Modelscited by 4
  6. 2025Graph Counterfactual Explainable AI via Latent Space Traversalcited by 3
  7. 2026Protein Counterfactuals via Diffusion-Guided Latent Optimizationcited by 3
  8. 2025A Survey on Interpretability in Visual Recognitioncited by 2
  9. 2025Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesiscited by 2
  10. 2024Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Modelscited by 1
  11. 2025Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Modelscited by 0
  12. 2026Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesiscited by 0
1 more
  1. 2026Towards Fast and Disentangled Counterfactuals for Visual Foundation Modelscited by 0