mlsanityData to 7 Oct 2026

Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos

arXiv 2311.00469 ↗First posted 1 Nov 2023cs.CV cs.AI cs.LGcited by 12

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Out-of-distribution (OOD) detection is essential to improve the reliability of machine learning models by detecting samples that do not belong to the training distribution. Detecting OOD samples effectively in certain tasks can pose a challenge because of the substantial heterogeneity within the in-distribution (ID), and the high structural similarity between ID and OOD classes. For instance, when detecting heart views in fetal ultrasound videos there is a high structural similarity between the heart and other anatomies such as the abdomen, and large in-distribution variance as a heart has 5 distinct views and structural variations within each view. To detect OOD samples in this context, the resulting model should generalise to the intra-anatomy variations while rejecting similar OOD samples. In this paper, we introduce dual-conditioned diffusion models (DCDM) where we condition the model on in-distribution class information and latent features of the input image for reconstruction-based OOD detection. This constrains the generative manifold of the model to generate images structurally and semantically similar to those within the in-distribution. The proposed model outperforms reference methods with a 12% improvement in accuracy, 22% higher precision, and an 8% better F1 score.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 30 of its 34 references that are in the map. First posted on arXiv 1 Nov 2023.

Builds on

30 of 34 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Denoising diffusion models for out-of-distribution detection14 Nov

Other references · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  7. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  8. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  9. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  10. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  11. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  12. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
17 more
  1. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  2. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  3. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  4. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  5. 2021Exploring the Limits of Out-of-Distribution Detectioncited by 283
  6. 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
  7. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  8. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  9. 2019Seeing What a GAN Cannot Generatecited by 148
  10. 2021On Calibration and Out-of-domain Generalizationcited by 134
  11. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  12. 2022Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detectioncited by 59
  13. 2022Attention Beats Concatenation for Conditioning Neural Fieldscited by 20
  14. 2021Multi-Class Data Description for Out-of-distribution Detectioncited by 19
  15. 2022Adversarial vulnerability of powerful near out-of-distribution detectioncited by 16
  16. 2019Attention-based Conditioning Methods for External Knowledge Integrationcited by 8
  17. 2020BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertaintycited by 7

Built on by

Cited by year

Citing papers per year: 2024: 6; 2025: 6.

12 papers.

  1. 2024Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessmentcited by 13
  2. 2024Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Modelscited by 6
  3. 2025Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillationcited by 4
  4. 2024NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Visioncited by 3
  5. 2025High Volume Rate 3D Ultrasound Reconstruction with Diffusion Modelscited by 2
  6. 2024Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Modelscited by 1
  7. 2025Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?cited by 1
  8. 2024Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplificationcited by 0
  9. 2024LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusioncited by 0
  10. 2025Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Maskingcited by 0
  11. 2025Enhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection Modulecited by 0
  12. 2025Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasoundcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories
  2. 2024NODI: Out-Of-Distribution Detection with Noise from Diffusion
  3. 2025GOOD: Training-Free Guided Diffusion Sampling for Out-of-Distribution Detection
  4. 2023Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
  5. 2024Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations
  6. 2025Delving into Out-of-Distribution Detection with Medical Vision-Language Models
  7. 2026KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems
  8. 2026Geometry over Density: Few-Shot Cross-Domain OOD Detection