mlsanityData to 14 Oct 2026

Pyramid Hierarchical Masked Diffusion Model for Imaging Synthesis

arXiv 2507.16579 ↗First posted 22 Jul 2025eess.IV cs.AI cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Medical image synthesis plays a crucial role in clinical workflows, addressing the common issue of missing imaging modalities due to factors such as extended scan times, scan corruption, artifacts, patient motion, and intolerance to contrast agents. The paper presents a novel image synthesis network, the Pyramid Hierarchical Masked Diffusion Model (PHMDiff), which employs a multi-scale hierarchical approach for more detailed control over synthesizing high-quality images across different resolutions and layers. Specifically, this model utilizes randomly multi-scale high-proportion masks to speed up diffusion model training, and balances detail fidelity and overall structure. The integration of a Transformer-based Diffusion model process incorporates cross-granularity regularization, modeling the mutual information consistency across each granularity's latent spaces, thereby enhancing pixel-level perceptual accuracy. Comprehensive experiments on two challenging datasets demonstrate that PHMDiff achieves superior performance in both the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), highlighting its capability to produce high-quality synthesized images with excellent structural integrity. Ablation studies further confirm the contributions of each component. Furthermore, the PHMDiff model, a multi-scale image synthesis framework across and within medical imaging modalities, shows significant advantages over other methods. The source code is available at https://github.com/xiaojiao929/PHMDiff

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 25 of its 42 references that are in the map. First posted on arXiv 22 Jul 2025.

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25 of 42 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis7 Jan
  2. 2023CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis24 Mar
  3. 2022Diffusion Probabilistic Models beat GANs on Medical Images14 Dec

Other references · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  7. 2022Scalable Diffusion Models with Transformerscited by 6,054
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  9. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  10. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  11. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  12. 2015Generative Moment Matching Networkscited by 257
10 more
  1. 2023Implicit Diffusion Models for Continuous Super-Resolutioncited by 218
  2. 2016EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesiscited by 178
  3. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 132
  4. 2019Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Networkcited by 98
  5. 2023Diffusion Models as Masked Autoencoderscited by 71
  6. 2023Unified Multi-Modal Image Synthesis for Missing Modality Imputationcited by 40
  7. 2021VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformerscited by 28
  8. 2020GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesiscited by 15
  9. 2021PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformercited by 11
  10. 2021Six-channel Image Representation for Cross-domain Object Detectioncited by 8

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  2. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
  3. 2024Ultra-High-Resolution Image Synthesis with Pyramid Diffusion Model
  4. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
  5. 2026Scale-Cascaded Diffusion Models for Super-Resolution in Medical Imaging
  6. 2026Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions
  7. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
  8. 2024McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis