mlsanityData to 7 Oct 2026

Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis

arXiv 2606.25390 ↗First posted 24 Jun 2026cs.CV cs.AIcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Anatomically-conditioned Latent Diffusion Model (ALDM), a novel framework for data-efficient, few-shot 3D volumetric MRI synthesis. ALDM utilizes a two-stage approach: a 3D variational autoencoder learns anatomical priors from a data-rich source domain, while a conditional latent diffusion model, guided by tumor masks via a ControlNet, generates structurally coherent volumes for a data-scarce target domain. Evaluated in an extreme few-shot setting with only 16 target images, ALDM outperformed GAN and hybrid baselines, achieving a superior Frechet Inception Distance (FID) of 85.40 and a downstream classification AUC of 0.987. Qualitative results confirm that the model preserves sharp pathology boundaries and cross-modal consistency, with visual fidelity improving progressively during training. By capturing essential diagnostic features, ALDM provides a robust tool for clinical data augmentation in low-resource settings. Our implementation is available at https://github.com/Analytics-Everywhere-Lab/anatomically-conditioned-LDM.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 10 of its 21 references that are in the map. First posted on arXiv 24 Jun 2026.

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10 of 21 references.

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  1. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis29 May

Other references · 9

  1. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  2. 2014Conditional Generative Adversarial Netscited by 2,152
  3. 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
  4. 2016Unsupervised domain adaptation in brain lesion segmentation with adversarial networkscited by 125
  5. 2021The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) Datasetcited by 42
  6. 2020Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesiscited by 12
  7. 2023Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networkscited by 6
  8. 2024IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR imagescited by 4
  9. 2024Exploring Adult Glioma through MRI: A Review of Publicly Available Datasets to Guide Efficient Image Analysiscited by 3

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  1. 2023Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
  2. 20243D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
  3. 2025Lesion-Aware Post-Training of Latent Diffusion Models for Synthesizing Diffusion MRI from CT Perfusion
  4. 2023CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis
  5. 2026A continually expandable foundation model for brain MRI
  6. 2026Unified Multi-plane Autoregressive Diffusion for 3D Multi-contrast MRI Synthesis
  7. 2026BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation
  8. 2025VGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentation