# BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2304.04429 · HTML: https://mlsanity.com/paper/2304.04429/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Placed in

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

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions](https://mlsanity.com/paper/2102.05379/) (2021)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [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)
- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [PHiSeg: Capturing Uncertainty in Medical Image Segmentation](https://mlsanity.com/paper/1906.04045/) (2019)
- [A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities](https://mlsanity.com/paper/1905.13077/) (2019)
- [Uncertainty quantification in medical image segmentation with normalizing flows](https://mlsanity.com/paper/2006.02683/) (2020)
- [Calibrated Adversarial Refinement for Stochastic Semantic Segmentation](https://mlsanity.com/paper/2006.13144/) (2020)

## Built on by (most cited first, 27 of 27)

- [Contrastive Conditional Latent Diffusion for Audio-visual Segmentation](https://mlsanity.com/paper/2307.16579/) (2023)
- [FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on](https://mlsanity.com/paper/2404.14162/) (2024)
- [HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation](https://mlsanity.com/paper/2407.03548/) (2024)
- [A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI](https://mlsanity.com/paper/2311.11383/) (2023)
- [RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation](https://mlsanity.com/paper/2306.08947/) (2023)
- [Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2403.11667/) (2024)
- [Generative Medical Segmentation](https://mlsanity.com/paper/2403.18198/) (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)
- [Physics-Inspired Generative Models in Medical Imaging: A Review](https://mlsanity.com/paper/2407.10856/) (2024)
- [A Comprehensive Survey on Diffusion Models and Their Applications](https://mlsanity.com/paper/2408.10207/) (2024)
- [Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction](https://mlsanity.com/paper/2502.20784/) (2025)
- [BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis](https://mlsanity.com/paper/2504.04532/) (2025)
- [Denoising Diffusion Models for Anomaly Localization in Medical Images](https://mlsanity.com/paper/2410.23834/) (2024)
- [Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging](https://mlsanity.com/paper/2307.16694/) (2023)
- [TrustLDM: Benchmarking Trustworthiness in Language Diffusion Models](https://mlsanity.com/paper/2606.00023/) (2026)
- [Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging](https://mlsanity.com/paper/2405.06175/) (2024)
- [Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models](https://mlsanity.com/paper/2505.02753/) (2025)
- [Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2505.07866/) (2025)
- [Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training](https://mlsanity.com/paper/2504.01547/) (2025)
- [Echo-DND: A dual noise diffusion model for robust and precise left ventricle segmentation in echocardiography](https://mlsanity.com/paper/2506.15166/) (2025)
- [Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2603.04024/) (2026)
- [Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methods](https://mlsanity.com/paper/2603.20188/) (2026)
- [Deterministic Mode Proposals: An Efficient Alternative to Generative Sampling for Ambiguous Segmentation](https://mlsanity.com/paper/2603.20191/) (2026)
- [Generative AI and Foundation Models in Medical Image](https://mlsanity.com/paper/2608.01686/) (2026)
- [Image-Conditioned Diffusion Models for Quality Assurance of Organ-at-Risk Segmentations in Radiotherapy](https://mlsanity.com/paper/2608.23432/) (2026)
- [Rethinking Diffusion Segmentation: When Does It Rely on Its Noisy State, and Does Diffusion Matter?](https://mlsanity.com/paper/2609.23967/) (2026)

## Abstract (arXiv)

Medical image segmentation is a challenging task with inherent ambiguity and high uncertainty, attributed to factors such as unclear tumor boundaries and multiple plausible annotations. The accuracy and diversity of segmentation masks are both crucial for providing valuable references to radiologists in clinical practice. While existing diffusion models have shown strong capacities in various visual generation tasks, it is still challenging to deal with discrete masks in segmentation. To achieve accurate and diverse medical image segmentation masks, we propose a novel conditional Bernoulli Diffusion model for medical image segmentation (BerDiff). Instead of using the Gaussian noise, we first propose to use the Bernoulli noise as the diffusion kernel to enhance the capacity of the diffusion model for binary segmentation tasks, resulting in more accurate segmentation masks. Second, by leveraging the stochastic nature of the diffusion model, our BerDiff randomly samples the initial Bernoulli noise and intermediate latent variables multiple times to produce a range of diverse segmentation masks, which can highlight salient regions of interest that can serve as valuable references for radiologists. In addition, our BerDiff can efficiently sample sub-sequences from the overall trajectory of the reverse diffusion, thereby speeding up the segmentation process. Extensive experimental results on two medical image segmentation datasets with different modalities demonstrate that our BerDiff outperforms other recently published state-of-the-art methods. Our results suggest diffusion models could serve as a strong backbone for medical image segmentation.