mlsanityData to 7 Oct 2026

Spatial Diffusion for Cell Layout Generation

arXiv 2409.03106 ↗First posted 4 Sep 2024cs.CVcited by 5

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Generative models, such as GANs and diffusion models, have been used to augment training sets and boost performances in different tasks. We focus on generative models for cell detection instead, i.e., locating and classifying cells in given pathology images. One important information that has been largely overlooked is the spatial patterns of the cells. In this paper, we propose a spatial-pattern-guided generative model for cell layout generation. Specifically, a novel diffusion model guided by spatial features and generates realistic cell layouts has been proposed. We explore different density models as spatial features for the diffusion model. In downstream tasks, we show that the generated cell layouts can be used to guide the generation of high-quality pathology images. Augmenting with these images can significantly boost the performance of SOTA cell detection methods. The code is available at https://github.com/superlc1995/Diffusion-cell.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 38 of its 58 references that are in the map. First posted on arXiv 4 Sep 2024.

Builds on

38 of 58 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Label-Efficient Semantic Segmentation with Diffusion Models6 Dec

Other references · 37

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  7. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  8. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  9. 2018Self-Attention Generative Adversarial Networkscited by 809
  10. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  11. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  12. 2022Perception Prioritized Training of Diffusion Modelscited by 301
25 more
  1. 2022Flexible Diffusion Modeling of Long Videoscited by 294
  2. 2018cGANs with Projection Discriminatorcited by 253
  3. 2022Diffusion models as plug-and-play priorscited by 230
  4. 2019Topology-Preserving Deep Image Segmentationcited by 198
  5. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 141
  6. 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
  7. 2021Instance-Conditioned GANcited by 119
  8. 2021Variational Transformer Networks for Layout Generationcited by 88
  9. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  10. 2021Topology-Aware Segmentation Using Discrete Morse Theorycited by 75
  11. 2023LayoutDM: Transformer-based Diffusion Model for Layout Generationcited by 57
  12. 2021Trigger Hunting with a Topological Prior for Trojan Detectioncited by 39
  13. 2023Topology-Aware Uncertainty for Image Segmentationcited by 32
  14. 2022Learning Topological Interactions for Multi-Class Medical Image Segmentationcited by 28
  15. 2021Multi-Class Cell Detection Using Spatial Context Representationcited by 24
  16. 2021Structure-Aware Image Segmentation with Homotopy Warpingcited by 24
  17. 2023Calibrating Uncertainty for Semi-Supervised Crowd Countingcited by 22
  18. 2022Learning Probabilistic Topological Representations Using Discrete Morse Theorycited by 20
  19. 2023Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentationcited by 14
  20. 2023Affine-Consistent Transformer for Multi-Class Cell Nuclei Detectioncited by 12
  21. 2023Confidence Estimation Using Unlabeled Datacited by 8
  22. 2023Topology-Guided Multi-Class Cell Context Generation for Digital Pathologycited by 7
  23. 2020Improved Inference of Gaussian Mixture Copula Model for Clustering and Reproducibility Analysis using Automatic Differentiationcited by 4
  24. 2022Deep Statistic Shape Model for Myocardium Segmentationcited by 3
  25. 2024P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRIcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2024TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Modelcited by 16
  2. 2025Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challengescited by 5
  3. 2025PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Designcited by 4
  4. 2025Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDAcited by 0
  5. 2025Reducing Domain Gap with Diffusion-Based Domain Adaptation for Cell Countingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Tertiary Lymphoid Structures Generation through Graph-based Diffusion
  2. 2024Comparative Analysis of Diffusion Generative Models in Computational Pathology
  3. 2026Layout-Guided Controllable Pathology Image Generation with In-Context Diffusion Transformers
  4. 2022A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
  5. 2025Diffusion-Based Synthetic Brightfield Microscopy Images for Enhanced Single Cell Detection
  6. 2025PixCell: A generative foundation model for digital histopathology images
  7. 2025Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data
  8. 2024Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield