# Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation

arXiv: https://arxiv.org/abs/2505.06068 · HTML: https://mlsanity.com/paper/2505.06068/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 61 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 30 of 50)

- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [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)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021)
- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [Rethinking FID: Towards a Better Evaluation Metric for Image Generation](https://mlsanity.com/paper/2401.09603/) (2023)
- [VOS: Learning What You Don't Know by Virtual Outlier Synthesis](https://mlsanity.com/paper/2202.01197/) (2022)

## Built on by (most cited first, 23 of 23)

- [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://mlsanity.com/paper/2505.17685/) (2025)
- [TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency](https://mlsanity.com/paper/2507.00802/) (2025)
- [Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis](https://mlsanity.com/paper/2507.23652/) (2025)
- [DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance](https://mlsanity.com/paper/2606.08906/) (2026)
- [Object Fidelity Diffusion for Remote Sensing Image Generation](https://mlsanity.com/paper/2508.10801/) (2025)
- [CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation](https://mlsanity.com/paper/2603.12008/) (2026)
- [LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images](https://mlsanity.com/paper/2506.17983/) (2025)
- [SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model](https://mlsanity.com/paper/2507.05148/) (2025)
- [Rep-GLS: Report-Guided Generalized Label Smoothing for Robust Disease Detection](https://mlsanity.com/paper/2508.02495/) (2025)
- [PairUni: Pairwise Training for Unified Multimodal Language Models](https://mlsanity.com/paper/2510.25682/) (2025)
- [MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis](https://mlsanity.com/paper/2602.17901/) (2026)
- [Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesis](https://mlsanity.com/paper/2602.18400/) (2026)
- [Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport](https://mlsanity.com/paper/2605.08851/) (2026)
- [Crucial-Diff: A Unified Diffusion Model for Crucial Image and Annotation Synthesis in Data-scarce Scenarios](https://mlsanity.com/paper/2507.09915/) (2025)
- [MVT: Mask-Grounded Vision-Language Models for Taxonomy-Aligned Land-Cover Tagging](https://mlsanity.com/paper/2509.18693/) (2025)
- [BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet](https://mlsanity.com/paper/2511.12853/) (2025)
- [Training-free Clothing Region of Interest Self-correction for Virtual Try-On](https://mlsanity.com/paper/2512.07126/) (2025)
- [CF-Net: A Cross-Feature Reconstruction Network for High-Accuracy 1-Bit Target Classification](https://mlsanity.com/paper/2512.15105/) (2025)
- [Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networks](https://mlsanity.com/paper/2602.02171/) (2026)
- [LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation](https://mlsanity.com/paper/2603.04795/) (2026)
- [Few-Shot Distribution-Aligned Flow Matching for Data Synthesis in Medical Image Segmentation](https://mlsanity.com/paper/2604.02868/) (2026)
- [Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting](https://mlsanity.com/paper/2607.02637/) (2026)
- [Temporal Diffusion Bridge Model for Vessel Extraction Using Physics-informed Synthetic Data](https://mlsanity.com/paper/2610.14105/) (2026)

## Abstract (arXiv)

Deep learning has revolutionized medical image segmentation, yet its full potential remains constrained by the paucity of annotated datasets. While diffusion models have emerged as a promising approach for generating synthetic image-mask pairs to augment these datasets, they paradoxically suffer from the same data scarcity challenges they aim to mitigate. Traditional mask-only models frequently yield low-fidelity images due to their inability to adequately capture morphological intricacies, which can critically compromise the robustness and reliability of segmentation models. To alleviate this limitation, we introduce Siamese-Diffusion, a novel dual-component model comprising Mask-Diffusion and Image-Diffusion. During training, a Noise Consistency Loss is introduced between these components to enhance the morphological fidelity of Mask-Diffusion in the parameter space. During sampling, only Mask-Diffusion is used, ensuring diversity and scalability. Comprehensive experiments demonstrate the superiority of our method. Siamese-Diffusion boosts SANet's mDice and mIoU by 3.6% and 4.4% on the Polyps, while UNet improves by 1.52% and 1.64% on the ISIC2018. Code is available at GitHub.