# Stochastic Segmentation with Conditional Categorical Diffusion Models

arXiv: https://arxiv.org/abs/2303.08888 · HTML: https://mlsanity.com/paper/2303.08888/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 51 of its 59 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Placed in

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

## Builds on (most cited first, 30 of 51)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions](https://mlsanity.com/paper/2102.05379/) (2021)
- [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://mlsanity.com/paper/2104.13840/) (2021)
- [Label-Efficient Semantic Segmentation with Diffusion Models](https://mlsanity.com/paper/2112.03126/) (2021)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning](https://mlsanity.com/paper/2208.04202/) (2022)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021)

## Built on by (most cited first, 30 of 38)

- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [Contrastive Conditional Latent Diffusion for Audio-visual Segmentation](https://mlsanity.com/paper/2307.16579/) (2023)
- [Diffusion-based Blind Text Image Super-Resolution](https://mlsanity.com/paper/2312.08886/) (2023)
- [Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis](https://mlsanity.com/paper/2410.02321/) (2024)
- [Tyche: Stochastic In-Context Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.13650/) (2024)
- [FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection](https://mlsanity.com/paper/2407.13133/) (2024)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models](https://mlsanity.com/paper/2308.16355/) (2023)
- [Generative Edge Detection with Stable Diffusion](https://mlsanity.com/paper/2410.03080/) (2024)
- [Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching](https://mlsanity.com/paper/2507.22418/) (2025)
- [Ambiguous Medical Image Segmentation Using Diffusion Schr"odinger Bridge](https://mlsanity.com/paper/2509.17187/) (2025)
- [RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models](https://mlsanity.com/paper/2410.00307/) (2024)
- [Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2403.10931/) (2024)
- [Denoising Diffusion Models for Anomaly Localization in Medical Images](https://mlsanity.com/paper/2410.23834/) (2024)
- [Flow Stochastic Segmentation Networks](https://mlsanity.com/paper/2507.18838/) (2025)
- [Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging](https://mlsanity.com/paper/2307.16694/) (2023)
- [DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model](https://mlsanity.com/paper/2507.13087/) (2025)
- [Sequential Amodal Segmentation via Cumulative Occlusion Learning](https://mlsanity.com/paper/2405.05791/) (2024)
- [Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging](https://mlsanity.com/paper/2405.06175/) (2024)
- [MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification](https://mlsanity.com/paper/2503.12401/) (2025)
- [Ambiguity-aware Truncated Flow Matching for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2511.06857/) (2025)
- [Unlocking Diffusion Hierarchies: Adaptive Timestep Selection for Zero-Shot Segmentation](https://mlsanity.com/paper/2606.15590/) (2026)
- [ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning](https://mlsanity.com/paper/2312.01682/) (2023)
- [GuideGen: A Text-Guided Framework for Paired Full-torso Anatomy and CT Volume Generation](https://mlsanity.com/paper/2403.07247/) (2024)
- [Panoptic Segmentation of Mammograms with Text-To-Image Diffusion Model](https://mlsanity.com/paper/2407.14326/) (2024)
- [Denoising diffusion models for high-resolution microscopy image restoration](https://mlsanity.com/paper/2409.12078/) (2024)
- [Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains](https://mlsanity.com/paper/2512.13534/) (2025)
- [Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2603.04024/) (2026)
- [Structure and Progress Aware Diffusion for Medical Image Segmentation](https://mlsanity.com/paper/2603.07889/) (2026)
- [Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methods](https://mlsanity.com/paper/2603.20188/) (2026)

## Abstract (arXiv)

Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving. Instead, multiple possible correct segmentation maps may be required to reflect the true distribution of annotation maps. In this context, stochastic semantic segmentation methods must learn to predict conditional distributions of labels given the image, but this is challenging due to the typically multimodal distributions, high-dimensional output spaces, and limited annotation data. To address these challenges, we propose a conditional categorical diffusion model (CCDM) for semantic segmentation based on Denoising Diffusion Probabilistic Models. Our model is conditioned to the input image, enabling it to generate multiple segmentation label maps that account for the aleatoric uncertainty arising from divergent ground truth annotations. Our experimental results show that CCDM achieves state-of-the-art performance on LIDC, a stochastic semantic segmentation dataset, and outperforms established baselines on the classical segmentation dataset Cityscapes.