mlsanityData to 7 Oct 2026

Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction

arXiv 2308.14409 ↗First posted 28 Aug 2023cs.CV cs.LGcited by 15

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Denoising diffusion models have emerged as the go-to generative framework for solving inverse problems in imaging. A critical concern regarding these models is their performance on out-of-distribution tasks, which remains an under-explored challenge. Using a diffusion model on an out-of-distribution dataset, realistic reconstructions can be generated, but with hallucinating image features that are uniquely present in the training dataset. To address this discrepancy during train-test time and improve reconstruction accuracy, we introduce a novel sampling framework called Steerable Conditional Diffusion. Specifically, this framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement. Utilising our proposed method, we achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities, advancing the robust deployment of denoising diffusion models in real-world applications.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 46 of its 71 references that are in the map. First posted on arXiv 28 Aug 2023.

Builds on

46 of 71 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Score-Based Generative Models for PET Image Reconstruction27 Aug
  2. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models19 Nov
  3. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
  4. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 42

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  7. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  8. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  9. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  10. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  11. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,507
  12. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
30 more
  1. 2017Deep Image Priorcited by 1,097
  2. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 574
  3. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  4. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  5. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
  6. 2023FreeDoM: Training-Free Energy-Guided Conditional Diffusion Modelcited by 290
  7. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  8. 2017Learned Primal-dual Reconstructioncited by 251
  9. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 218
  10. 2023A Variational Perspective on Solving Inverse Problems with Diffusion Modelscited by 217
  11. 2023Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMCcited by 216
  12. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistencycited by 180
  13. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  14. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imagingcited by 126
  15. 2018Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networkscited by 120
  16. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 108
  17. 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
  18. 2023Direct Diffusion Bridge using Data Consistency for Inverse Problemscited by 65
  19. 2020Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challengecited by 59
  20. 2020Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methodscited by 56
  21. 2021Measuring Robustness in Deep Learning Based Compressive Sensingcited by 53
  22. 2023GSURE-Based Diffusion Model Training with Corrupted Datacited by 40
  23. 2022Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensingcited by 37
  24. 2021Regularising Inverse Problems with Generative Machine Learning Modelscited by 22
  25. 2019A Cone-Beam X-Ray CT Data Collection designed for Machine Learningcited by 21
  26. 2023Accelerating Diffusion Models for Inverse Problems through Shortcut Samplingcited by 21
  27. 2020Model Adaptation for Inverse Problems in Imagingcited by 20
  28. 2022ADIR: Adaptive Diffusion for Image Reconstructioncited by 20
  29. 2021An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstructioncited by 16
  30. 2020Fast Variable Density Poisson-Disc Sample Generation with Directional Variationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 7; 2026: 5.

15 papers.

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By area:

  1. 2025Diffusion models for inverse problemscited by 15
  2. 2024Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstructioncited by 9
  3. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  4. 2025PET Image Reconstruction Using Deep Diffusion Image Priorcited by 6
  5. 2025Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Modelscited by 4
  6. 2026Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performancecited by 4
  7. 2024Fast MRI for All: Bridging Access Gaps by Training without Raw Datacited by 2
  8. 2024An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse Problemscited by 1
  9. 2025Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CTcited by 1
  10. 2025Steerable Conditional Diffusion for Domain Adaptation in PET Image Reconstructioncited by 1
  11. 2025Diffusion-Guided Mask-Consistent Paired Mixing for Endoscopic Image Segmentationcited by 1
  12. 2026Solving Inverse Problems with Flow-based Models via Model Predictive Controlcited by 1
3 more
  1. 2026PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstructioncited by 1
  2. 2026Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuningcited by 0
  3. 2026Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Modelcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  2. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction
  3. 2023Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision
  4. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
  5. 2024Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
  6. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
  7. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
  8. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction