# Graph Conditioned Diffusion for Controllable Histopathology Image Generation

arXiv: https://arxiv.org/abs/2510.07129 · HTML: https://mlsanity.com/paper/2510.07129/

## 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 16 of its 27 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 16 of 16)

- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Representation Learning of Histopathology Images using Graph Neural Networks](https://mlsanity.com/paper/2004.07399/) (2020)
- [Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis](https://mlsanity.com/paper/2303.12644/) (2023)
- [Image Generation Diversity Issues and How to Tame Them](https://mlsanity.com/paper/2411.16171/) (2024)
- [NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models](https://mlsanity.com/paper/2303.11477/) (2023)
- [Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction](https://mlsanity.com/paper/2205.06672/) (2022)
- [Synthetic Sample Selection via Reinforcement Learning](https://mlsanity.com/paper/2008.11331/) (2020)
- [Realistic Data Enrichment for Robust Image Segmentation in Histopathology](https://mlsanity.com/paper/2304.09534/) (2023)
- [Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics](https://mlsanity.com/paper/2406.13652/) (2024)
- [Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting](https://mlsanity.com/paper/2306.01363/) (2023)
- [URCDM: Ultra-Resolution Image Synthesis in Histopathology](https://mlsanity.com/paper/2407.13277/) (2024)

## Abstract (arXiv)

Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sensitive areas such as medical imaging. Medical images feature inherent structure such as consistent spatial arrangement, shape or texture, all of which are critical for diagnosis. However, existing DPMs operate in noisy latent spaces that lack semantic structure and strong priors, making it difficult to ensure meaningful control over generated content. To address this, we propose graph-based object-level representations for Graph-Conditioned-Diffusion. Our approach generates graph nodes corresponding to each major structure in the image, encapsulating their individual features and relationships. These graph representations are processed by a transformer module and integrated into a diffusion model via the text-conditioning mechanism, enabling fine-grained control over generation. We evaluate this approach using a real-world histopathology use case, demonstrating that our generated data can reliably substitute for annotated patient data in downstream segmentation tasks. The code is available here.