mlsanityData to 14 Oct 2026

MedDiME: Efficient Latent Diffusion with Adaptive Masking for Medical Counterfactual Generation

arXiv 2609.15647 ↗First posted 14 Sep 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Medical counterfactual generation modifies images to change model predictions for interpretability. However, existing diffusion-based approaches are often prohibitively slow and memory-intensive, making them difficult to apply in high-resolution settings. Moreover, existing masking strategies are tightly coupled with pixel-space representations, making them incompatible with latent-space diffusion editing. To address these challenges, we propose MedDiME, a latent-space classifier-guided diffusion framework that reduces computational and memory overhead while introducing a latent-compatible, gradient-driven adaptive masking mechanism for spatially precise medical counterfactual generation. Extensive experiments demonstrate that MedDiME achieves high-quality counterfactual generation with significant efficiency gains compared to prior classifier-guided diffusion baselines, achieving up to 40 times faster inference and 13 times lower peak GPU memory usage.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 24 of its 27 references that are in the map. First posted on arXiv 14 Sep 2026.

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24 of 27 references.

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Same direction, newest first

  1. 2024Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis30 Dec

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2016Densely Connected Convolutional Networkscited by 6,720
  6. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  7. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  8. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 669
  9. 2022Diffusion Visual Counterfactual Explanationscited by 96
  10. 2022Diffusion Models for Counterfactual Explanationscited by 79
  11. 2019Explanation by Progressive Exaggerationcited by 76
  12. 2023Adversarial Counterfactual Visual Explanationscited by 47
11 more
  1. 2023Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generationcited by 41
  2. 2021Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative modelscited by 36
  3. 2022Cycle-Consistent Counterfactuals by Latent Transformationscited by 33
  4. 2023Text-to-Image Models for Counterfactual Explanations: a Black-Box Approachcited by 28
  5. 2024Benchmarking Counterfactual Image Generationcited by 22
  6. 2023Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Modelscited by 17
  7. 2024Rethinking Visual Counterfactual Explanations Through Region Constraintcited by 14
  8. 2023Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluationcited by 6
  9. 2024The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanationscited by 5
  10. 2025From Visual Explanations to Counterfactual Explanations with Latent Diffusioncited by 4
  11. 2026MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanationscited by 2

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  1. 2025PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
  2. 2024MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI
  3. 2023BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
  4. 2026Generating Medical Image Counterfactuals using Causal Explanations
  5. 2024Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs
  6. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
  7. 2025Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance
  8. 2025Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation