mlsanityData to 7 Oct 2026

MedCondDiff: Lightweight, Robust, Semantically Guided Diffusion for Medical Image Segmentation

arXiv 2512.00350 ↗First posted 29 Nov 2025eess.IV cs.AI cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

We introduce MedCondDiff, a diffusion-based framework for multi-organ medical image segmentation that is efficient and anatomically grounded. The model conditions the denoising process on semantic priors extracted by a Pyramid Vision Transformer (PVT) backbone, yielding a semantically guided and lightweight diffusion architecture. This design improves robustness while reducing both inference time and VRAM usage compared to conventional diffusion models. Experiments on multi-organ, multi-modality datasets demonstrate that MedCondDiff delivers competitive performance across anatomical regions and imaging modalities, underscoring the potential of semantically guided diffusion models as an effective class of architectures for medical imaging tasks.

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 20 of its 24 references that are in the map. First posted on arXiv 29 Nov 2025.

Builds on

20 of 24 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation18 Mar
  2. 2022DiffusionInst: Diffusion Model for Instance Segmentation6 Dec
  3. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov
  4. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Models1 Dec

Other references · 16

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  7. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  8. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  9. 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,439
  10. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,339
  11. 2022All are Worth Words: A ViT Backbone for Diffusion Modelscited by 498
  12. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
4 more
  1. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 222
  2. 2024Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A surveycited by 13
  3. 2025Recent Advances in Medical Imaging Segmentation: A Surveycited by 8
  4. 2024ConSept: Continual Semantic Segmentation via Adapter-based Vision Transformercited by 3

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  1. 2025Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentation
  2. 2024MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications
  3. 2025VGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentation
  4. 2026ProGiDiff: Prompt-Guided Diffusion-Based Medical Image Segmentation
  5. 2025Medical Semantic Segmentation with Diffusion Pretrain
  6. 2025TextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation
  7. 2024Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models
  8. 2023DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model