Awesome Diffusion Models in Medical Imaging
:fire::fire: This is a collection of awesome articles about diffusion models in medical imaging:fire::fire:
- Our survey paper on MedIA: Diffusion Models in Medical Imaging: A Comprehensive Survey :heart:
- Our survey paper on arXiv: Diffusion Models for Medical Image Analysis: A Comprehensive Survey :heart:
Citation
@article{kazerouni2023diffusion,
title={Diffusion models in medical imaging: A comprehensive survey},
author={Kazerouni, Amirhossein and Aghdam, Ehsan Khodapanah and Heidari, Moein and Azad, Reza and Fayyaz, Mohsen and Hacihaliloglu, Ilker and Merhof, Dorit},
journal={Medical Image Analysis},
pages={102846},
year={2023},
publisher={Elsevier}
}
Updates
- We have now achieved more than 1K stars 🌟—thank you community for your support! If you're interested in contributing to this repository, please don't hesitate to send me a message. Thank you!
- Check out our new paper accepted in MICCAI 2023 PRIME Workshop: DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation 🥳
- Third release: June 3, 2023
- :sunglasses: April 8, 2023: Our paper is accepted for publication in the Medical Image Analysis Journal (IF: 13.83) :sunglasses:
- Second release: March 29, 2023
- First release: November 14, 2022
Contents
-
- Anomaly Detection
- Denoising
- Segmentation
- Image-to-Image Translation
- Reconstruction
- Image Generation
- Text-to-Image
- Registration
- Classification
- Object Detection
- Image Restoration
- Editing
- Adversarial Attacks
- Fairness
- Time Series
- Audio
- Other Applications
- Multi-task
- Multimodal Generation & Reconstruction
Survey Papers
Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation
Xuanru Zhou, Cheng Li, Shuqiang Wang, Ye Li, Tao Tan, Hairong Zheng, Shanshan Wang
[07th Aug., 2025] [arXiv, 2025]
[Paper]
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
Abdullah, Tao Huang, Ickjai Lee, Euijoon Ahn
[09th May, 2025] [arXiv, 2025]
[Paper]
Diffusion Models in 3D Vision: A Survey
Zhen Wang, Dongyuan Li, Yaozu Wu, Tianyu He, Jiang Bian, Renhe Jiang
[07th Oct., 2024] [arXiv, 2024]
[Paper]
Foundation Models for Music: A Survey
Yinghao Ma, Anders Øland, Anton Ragni, Bleiz MacSen Del Sette, Charalampos Saitis, Chris Donahue, Chenghua Lin, Christos Plachouras, Emmanouil Benetos, Elona Shatri, Fabio Morreale, Ge Zhang, György Fazekas, Gus Xia, Huan Zhang, Ilaria Manco, Jiawen Huang, Julien Guinot, Liwei Lin, Luca Marinelli, Max W. Y. Lam, Megha Sharma, Qiuqiang Kong, Roger B. Dannenberg, Ruibin Yuan, Shangda Wu, Shih-Lun Wu, Shuqi Dai, Shun Lei, Shiyin Kang, Simon Dixon, Wenhu Chen, Wenhao Huang, Xingjian Du, Xingwei Qu, Xu Tan, Yizhi Li, Zeyue Tian, Zhiyong Wu, Zhizheng Wu, Ziyang Ma, Ziyu Wang
[26th Aug., 2024] [arXiv, 2024]
[Paper] [GitHub] [Music Generation - Music Therapy & Medical Applications]
A Comprehensive Survey on Diffusion Models and Their Applications
Md Manjurul Ahsan, Shivakumar Raman, Yingtao Liu, Zahed Siddique
[1st Jul., 2024] [arXiv, 2024]
[Paper]
Physics-Inspired Generative Models in Medical Imaging: A Review
Dennis Hein, Afshin Bozorgpour, Dorit Merhof, Ge Wang
[15th Jul., 2024] [arXiv, 2024]
[Paper]
Diffusion Models in Low-Level Vision: A Survey
Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, Xiu Li
[16th Jun., 2024] [arXiv, 2024]
[Paper] [GitHub]
Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review
Mohammadreza Amirian, Daniel Barco, Ivo Herzig, Frank-Peter Schilling
[27th Mar., 2024] [arXiv, 2024]
[Paper]
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI
Yuheng Fan, Hanxi Liao, Shiqi Huang, Yimin Luo, Huazhu Fu, Haikun Qi
[18th Nov., 2023] [arXiv, 2023]
[Paper]
A Comprehensive Review of Generative AI in Healthcare
Yasin Shokrollahi, Sahar Yarmohammadtoosky, Matthew M. Nikahd, Pengfei Dong, Xianqi Li, Linxia Gu
[24th Jul., 2023] [arXiv, 2023]
[Paper]
Generative AI for Medical Imaging: extending the MONAI Framework :fire:
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb, Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
[27th Jul., 2023] [arXiv, 2023]
[Paper] [Github]
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan
[24th Jul., 2023] [Journal of Imaging, 2023]
[Paper]
A Comprehensive Survey on Generative Diffusion Models for Structured Data
Heejoon Koo, To Eun Kim
[7th Jun., 2023] [arXiv, 2023]
[Paper]
Diffusion Models for Time Series Applications: A Survey
Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao
[1st May, 2023] [arXiv, 2023]
[Paper]
A Comprehensive Survey on Knowledge Distillation of Diffusion Models
Weijian Luo
[9th Apr., 2023] [arXiv, 2023]
[Paper]
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
Mengchun Zhang, Maryam Qamar, Taegoo Kang, Yuna Jung, Chenshuang Zhang, Sung-Ho Bae, Chaoning Zhang
[4th Apr., 2023] [arXiv, 2023]
[Paper]
Audio Diffusion Model for Speech Synthesis: A Survey on Text To Speech and Speech Enhancement in Generative AI
Chenshuang Zhang, Chaoning Zhang, Sheng Zheng, Mengchun Zhang, Maryam Qamar, Sung-Ho Bae, In So Kweon
[23th Mar., 2023] [arXiv, 2023]
[Paper]
Diffusion Models in NLP: A Survey
Yuansong Zhu, Yu Zhao
[14th Mar., 2023] [arXiv, 2023]
[Paper]
Text-to-image Diffusion Model in Generative AI: A Survey
Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In So Kweon
[14th Mar., 2023] [arXiv, 2023]
[Paper]
Diffusion Models for Non-autoregressive Text Generation: A Survey
Yifan Li, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen
[12th Mar., 2023] [arXiv, 2023]
[Paper]
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action
Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu, Jianlin Cheng
[13th Feb., 2023] [arXiv, 2023]
[Paper]
Generative Diffusion Models on Graphs: Methods and Applications
Wenqi Fan, Chengyi Liu, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, Qing Li
[6th Feb., 2023] [arXiv, 2023]
[Paper]
Diffusion Models in Medical Imaging: A Comprehensive Survey :fire:
Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Mohsen Fayyaz, Ilker Hacihaliloglu, Dorit Merhof
[14th Nov., 2022] [MedIA Journal, 2023]
[Paper]
Efficient Diffusion Models for Vision: A Survey
Anwaar Ulhaq, Naveed Akhtar, Ganna Pogrebna
[7th Oct., 2022] [arXiv, 2022]
[Paper]
Diffusion Models in Vision: A Survey
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Mubarak Shah
[10th Sep., 2022] [arXiv, 2022]
[Paper] [Github]
A Survey on Generative Diffusion Model
Hanqun Cao, Cheng Tan, Zhangyang Gao, Guangyong Chen, Pheng-Ann Heng, Stan Z. Li
[6th Sep., 2022] [arXiv, 2022]
[Paper] [Github]
Diffusion Models: A Comprehensive Survey of Methods and Applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, Ming-Hsuan Yang
[2nd Sep., 2022] [arXiv, 2022]
[Paper] [Github]
Challenge Reports
Federated Learning for Medical Image Classification: A Comprehensive Benchmark
Zhekai Zhou, Guibo Luo, Mingzhi Chen, Zhenyu Weng, Yuesheng Zhu
[07th Apr., 2025] [arXiv, 2025]
[Paper]
Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics
Rucha Deshpande, Varun A. Kelkar, Dimitrios Gotsis, Prabhat Kc, Rongping Zeng, Kyle J. Myers, Frank J. Brooks, Mark A. Anastasio
[2nd May] [arXiv, 2024]
[Paper] [Website]
Papers
Anomaly Detection
Denoising Diffusion Models for Anomaly Localization in Medical Images
Cosmin I. Bercea, Philippe C. Cattin, Julia A. Schnabel, Julia Wolleb
[31th Oct., 2024] [arXiv, 2024]
[Paper]
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection
Finn Behrendt, Debayan Bhattacharya, Robin Mieling, Lennart Maack, Julia Krüger, Roland Opfer, Alexander Schlaefer
[03th Oct., 2024] [MICCAI, 2024]
[Paper] [GitHub]
CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion models
Ana Lawry Aguila, Ayodeji Ijishakin, Juan Eugenio Iglesias, Tomomi Takenaga, Yukihiro Nomura, Takeharu Yoshikawa, Osamu Abe, Shouhei Hanaoka
[05th Aug., 2025] [arXiv, 2025]
[Paper]
Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's disease
Hugues Roy, Reuben Dorent, Ninon Burgos
[23th Jul., 2025] [arXiv, 2025]
[Paper]
Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays
Harim Kim, Yuhan Wang, Minkyu Ahn, Heeyoul Choi, Yuyin Zhou, Charmgil Hong
[22th May., 2025] [arXiv, 2025]
[Paper]
HDM: Hybrid Diffusion Model for Unified Image Anomaly Detection
Zekang Weng, Jinjin Shi, Jinwei Wang, Zeming Han
[26th Feb., 2025] [arXiv, 2025]
[Paper]
MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection
Farzad Beizaee, Gregory Lodygensky, Christian Desrosiers, Jose Dolz
[24th Feb., 2025] [arXiv, 2025]
[Paper]
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
Yuan Bi, Lucie Huang, Ricarda Clarenbach, Reza Ghotbi, Angelos Karlas, Nassir Navab, Zhongliang Jiang
[06th Nov., 2024] [arXiv, 2024]
[Paper]
MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRI
Vivek Kumar Trivedi, Bheeshm Sharma, P. Balamurugan
[29th Sep., 2024] [arXiv, 2024]
[Paper] [GitHub]
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection
Finn Behrendt, Debayan Bhattacharya, Lennart Maack, Julia Krüger, Roland Opfer, Robin Mieling, Alexander Schlaefer
[22nd Aug., 2024] [ISBI, 2024]
Combining Reconstruction-based Unsupervised Anomaly Detection with Supervised Segmentation for Brain MRIs
Finn Behrendt, Debayan Bhattacharya, Lennart Maack, Julia Krüger, Roland Opfer, Alexander Schlaefer
[6th Jun., 2024] [MIDL, 2024]
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection
Julia Wolleb, Florentin Bieder, Paul Friedrich, Peter Zhang, Alicia Durrer, Philippe C. Cattin
[18th Mar., 2024] [arXiv, 2024]
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel
[13th Mar., 2024] [arXiv, 2024]
[Paper] [GitHub]
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model
Sangjoon Park, Yong Bae Kim, Jee Suk Chang, Seo Hee Choi, Hyungjin Chung, Ik Jae Lee, Hwa Kyung Byun
[28th Feb., 2024] [arXiv, 2024]
[Paper]
MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images
Rui Xu, Yunke Wang, Bo Du
[19th Jan., 2024] [arXiv, 2024]
[Paper]
Unsupervised Anomaly Detection using Aggregated Normative Diffusion
Alexander Frotscher, Jaivardhan Kapoor, Thomas Wolfers, Christian F. Baumgartner
[4th Dec., 2023] [arXiv, 2023]
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Sergio Naval Marimont, Matthew Baugh, Vasilis Siomos, Christos Tzelepis, Bernhard Kainz, Giacomo Tarroni
[26th Nov., 2023] [arXiv, 2023]
[Paper]
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
Finn Behrendt, Debayan Bhattacharya, Robin Mieling, Lennart Maack, Julia Krüger, Roland Opfer, Alexander Schlaefer
[7th Dec., 2023] [arXiv, 2023]
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection
Evi M.C. Huijben, Sina Amirrajab, Josien P.W. Pluim
[12th Oct., 2023] [arXiv, 2023]
AnoDODE: Anomaly Detection with Diffusion ODE
Xianyao Hu, Congming Jin
[10th Aug., 2023] [arXiv, 2023]
[Paper]
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
Ziyun Liang, Harry Anthony, Felix Wagner, Konstantinos Kamnitsas
[30th Aug., 2023] [arXiv, 2023]
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images
Alessandro Fontanella, Grant Mair, Joanna Wardlaw, Emanuele Trucco, Amos Storkey
[3rd Aug., 2023] [arXiv, 2023]
SANO: Score-Based Diffusion Model for Anomaly Localization in Dermatology
Alvaro Gonzalez-Jimenez, Simone Lionetti, Marc Pouly, Alexander A. Navarini
[18th Jun., 2023] [CVPR Workshop, 2023]
paper]
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models
Cosmin I. Bercea, Michael Neumayr, Daniel Rueckert, Julia A. Schnabel
[31st May, 2023] [arXiv, 2023]
[Paper]
Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model
Hasan Iqbal, Umar Khalid, Jing Hua, Chen Chen
[31st May, 2023] [MICCAI MLMI Workshop, 2023]
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection
Cosmin I Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A Schnabel
[15th Mar., 2023] [arXiv, 2023]
[Paper]
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI
Finn Behrendt, Debayan Bhattacharya, Julia Krüger, Roland Opfer, Alexander Schlaefer
[7th Mar., 2023] [MIDL, 2023]
Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection
Jian Shi, Pengyi Zhang, Ni Zhang, Hakim Ghazzai, Yehia Massoud
[28th Feb., 2023] [arXiv, 2023]
[Paper]
The role of noise in denoising models for anomaly detection in medical images
Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, Alison Q. O'Neil
[19th Jan., 2023] [MedIA Journal, 2023]
[Paper] [Github]
What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
Pedro Sanchez, Antanas Kascenas, Xiao Liu, Alison Q. O'Neil, Sotirios A. Tsaftaris
[25th Jul., 2022] [MICCAI Workshop, 2022]
[Paper] [Github]
AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise
Julian Wyatt, Adam Leach, Sebastian M. Schmon, Chris G. Willcocks
[1st Jun., 2022] [CVPR Workshop, 2022]
[Paper] [Github]
The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models
Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe C. Cattin
[6th Apr., 2022] [arXiv, 2022]
[Paper]
Diffusion Models for Medical Anomaly Detection
Julia Wolleb, Florentin Bieder, Robin Sandkühler, Philippe C. Cattin
[8th Mar., 2022] [MICCAI, 2022]
Denoising
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
Basar Demir, Yikang Liu, Xiao Chen, Eric Z. Chen, Lin Zhao, Boris Mailhe, Terrence Chen, Shanhui Sun
[31st Mar., 2025] [arXiv, 2025]
[Paper]
Ultrasound Imaging based on the Variance of a Diffusion Restoration Model
Yuxin Zhang, Clément Huneau, Jérôme Idier, Diana Mateus
[17th Jun., 2024] [EUSIPCO]
[Paper] [Github]
Dose-aware Diffusion Model for 3D Low-dose PET: Multi-institutional Validation with Reader Study and Real Low-dose Data
Huidong Xie, Weijie Gan, Bo Zhou, Ming-Kai Chen, Michal Kulon, Annemarie Boustani, Benjamin A. Spencer, Reimund Bayerlein, Xiongchao Chen, Qiong Liu, Xueqi Guo, Menghua Xia, Yinchi Zhou, Hui Liu, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Hanzhong Wang, Biao Li, Axel Rominger, Kuangyu Shi, Ge Wang, Ramsey D. Badawi, Chi Liu
[2nd May, 2024] [arXiv, 2024]
[Paper]
Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and Denoising
Yuang Wang, Siyeop Yoon, Pengfei Jin, Matthew Tivnan, Zhennong Chen, Rui Hu, Li Zhang, Zhiqiang Chen, Quanzheng Li, Dufan Wu
[10th Mar., 2024] [arXiv, 2024]
[Paper]
SDDPM: Speckle Denoising Diffusion Probabilistic Models
Soumee Guha, Scott T. Acton
[17th Nov., 2023] [arXiv, 2023]
[Paper]
Deep Ultrasound Denoising Using Diffusion Probabilistic Models
Hojat Asgariandehkordi, Sobhan Goudarzi, Adrian Basarab, Hassan Rivaz
[12th Jun., 2023] [arXiv, 2023]
[Paper]
A Diffusion Probabilistic Prior for Low-Dose CT Image Denoising
Xuan Liu, Yaoqin Xie, Songhui Diao, Shan Tan, Xiaokun Liang
[25th May, 2023] [arXiv, 2023]
[Paper]
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
Qi Gao, Zilong Li, Junping Zhang, Yi Zhang, Hongming Shan
[4th Apr., 2023] [arXiv, 2023]
[Paper]
DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Tiange Xiang, Mahmut Yurt, Ali B Syed, Kawin Setsompop, Akshay Chaudhari
[6th Feb., 2023] [ICLR, 2023]
[Paper] [Github]
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup
Wenjun Xia, Qing Lyu, Ge Wang
[29th Sep., 2022] [arXiv, 2022]
[Paper]
PET image denoising based on denoising diffusion probabilistic models
Kuang Gong, Keith A. Johnson, Georges El Fakhri, Quanzheng Li, Tinsu Pan
[13th Sep., 2022] [European Journal of Nuclear Medicine and Molecular Imaging, 2022]
[Paper]
Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model
Dewei Hu, Yuankai K. Tao, Ipek Oguz
[27th Jan., 2022] [Medical Imaging 2022: Image Processing]
[Paper] [Github]
Segmentation
Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching
Phi Van Nguyen, Ngoc Huynh Trinh, Duy Minh Lam Nguyen, Phu Loc Nguyen, Quoc Long Tran
[30th Jul., 2025] [arXiv, 2025]
[Paper] [Github]
Flow Stochastic Segmentation Networks
Fabio De Sousa Ribeiro, Omar Todd, Charles Jones, Avinash Kori, Raghav Mehta, Ben Glocker
[24th Jul., 2025] [arXiv, 2025]
[Paper] [Github]
LEAF: Latent Diffusion with Efficient Encoder Distillation for Aligned Features in Medical Image Segmentation
Qilin Huang, Tianyu Lin, Zhiguang Chen, Fudan Zheng
[24th Jul., 2025] [arXiv, 2025]
[Paper] [Github]
Robust Noisy Pseudo-label Learning for Semi-supervised Medical Image Segmentation Using Diffusion Model
Lin Xi, Yingliang Ma, Cheng Wang, Sandra Howell, Aldo Rinaldi, Kawal S. Rhode
[22nd Jul., 2025] [arXiv, 2025]
[Paper]
Latent Space Synergy: Text-Guided Data Augmentation for Direct Diffusion Biomedical Segmentation
Muhammad Aqeel, Maham Nazir, Zanxi Ruan, Francesco Setti
[21st Jul., 2025] [arXiv, 2025]
[Paper]
SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging
Salah Eddine Bekhouche, Gaby Maroun, Fadi Dornaika, Abdenour Hadid
[21st Jul., 2025] [arXiv, 2025]
[Paper] [Github]
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
Han Zhang, Xiangde Luo, Yong Chen, Kang Li
[17th Jul., 2025] [arXiv, 2025]
[Paper] [Github]
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Fangyijie Wang, Kevin Whelan, Félix Balado, Kathleen M. Curran, Guénolé Silvestre
[30th Jun., 2025] [arXiv, 2025]
[Paper]
Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation
Dewen Zeng, Xinrong Hu, Yu-Jen Chen, Yawen Wu, Xiaowei Xu, Yiyu Shi
[30th Jun., 2025] [arXiv, 2025]
[Paper]
Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation
Xinrong Hu, Yiyu Shi
[28th Jun., 2025] [arXiv, 2025]
[Paper]
CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentation
Farheen Ramzan, Yusuf Kiberu, Nikesh Jathanna, Shahnaz Jamil-Copley, Richard H. Clayton, Chen Chen
[18th Jun., 2025] [arXiv, 2025]
[Paper] [Github]
Echo-DND: A dual noise diffusion model for robust and precise left ventricle segmentation in echocardiography
Abdur Rahman, Keerthiveena Balraj, Manojkumar Ramteke, Anurag Singh Rathore
[18th Jun., 2025] [Discover Applied Sciences (Springer Nature), 2025]
[Paper] [Github] [Project Page]
Unleashing Diffusion and State Space Models for Medical Image Segmentation
Rong Wu, Ziqi Chen, Liming Zhong, Heng Li, Hai Shu
[15th Jun., 2025] [arXiv, 2025]
[Paper] [Github]
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
André Ferreira, Kunpeng Xie, Caroline Wilpert, Gustavo Correia, Felix Barajas Ordonez, Tiago Gil Oliveira, Maike Bode, Robert Siepmann, Frank Hölzle, Rainer Röhrig, Jens Kleesiek, Daniel Truhn, Jan Egger, Victor Alves, Behrus Puladi
[13th Jun., 2025] [arXiv, 2025]
[Paper] [Github]
TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Shengyuan Liu, Wenting Chen, Boyun Zheng, Wentao Pan, Xiang Li, Yixuan Yuan
[30th May, 2025] [arXiv, 2025]
[Paper]
Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization
Abderrachid Hamrani, Daniela Leizaola, Renato Sousa, Jose P. Ponce, Stanley Mathis, David G. Armstrong, Anuradha Godavarty
[24th Apr., 2025] [IEEE Journal of Biomedical and Health Informatics, 2025]
[Paper]
TextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation
Kangbo Ma
[16th Apr., 2025] [arXiv, 2025]
[Paper]
Diffusion Based Ambiguous Image Segmentation
Jakob Lønborg Christensen, Morten Rieger Hannemose, Anders Bjorholm Dahl, Vedrana Andersen Dahl
[08th Apr., 2025] [arXiv, 2025]
[Paper]
Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training
Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi
[2nd Apr., 2025] [arXiv, 2025] \
[Paper] [GitHub]
Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation
Xiuzhen Guo, Lianyuan Yu, Ji Shi, Na Lei, Hongxiao Wang
[12th Mar., 2025] [arXiv, 2025]
[Paper]
DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion
Hantao Zhang, Yuhe Liu, Jiancheng Yang, Weidong Guo, Xinyuan Wang, Pascal Fua
[09th Mar., 2025] [arXiv, 2025]
[Paper] [GitHub]
Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
Behzad Hejrati, Soumyanil Banerjee, Carri Glide-Hurst, Ming Dong
[10th Feb., 2025] [MICCAI, 2024]
[Paper] [GitHub]
Medical Semantic Segmentation with Diffusion Pretrain
David Li, Anvar Kurmukov, Mikhail Goncharov, Roman Sokolov, Mikhail Belyaev
[31st Jan., 2025] [arXiv, 2025]
[Paper]
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation
Avni Mittal, John Kalkhof, Anirban Mukhopadhyay, Arnav Bhavsar
[05th Jan., 2025] [arXiv, 2025]
[Paper]
Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models
Manh Duong Nguyen, Dac Thai Nguyen, Trung Viet Nguyen, Homi Yamada, Huy Hieu Pham, Phi Le Nguyen
[03rd Jan., 2025] [ISBI, 2025]
[Paper] [Github]
Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
Jie Bao, Zhixin Zhou, Wen Jung Li, Rui Luo
[05th Dec., 2024] [arXiv, 2024]
[Paper] [Github]
vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
Bastian Wittmann, Yannick Wattenberg, Tamaz Amiranashvili, Suprosanna Shit, Bjoern Menze
[26th Nov., 2024] [CVPR, 2025]
[Paper] [Github]
ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection
Tao Wang, Xinlin Zhang, Yuanbin Chen, Yuanbo Zhou, Longxuan Zhao, Tao Tan, Tong Tong
[15th Nov., 2024] [arXiv, 2024]
[Paper] [GitHub]
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DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays
Xuhui Liu, Zhi Qiao, Runkun Liu, Hong Li, Juan Zhang, Xiantong Zhen, Zhen Qian, Baochang Zhang
[18th Jul., 2024] [ECCV, 2025]
[Paper]
Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling
Noam Elata, Tomer Michaeli, Michael Elad
[11th Jul., 2024] [ECCV, 2024]
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Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Models
Jiayue Chu, Chenhe Du, Xiyue Lin, Yuyao Zhang, Hongjiang Wei
[2nd Jul., 2024] [MedIA, 2024]
[Paper]
Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction
Bin Huang, Xubiao Liu, Lei Fang, Qiegen Liu, Bingxuan Li
[30 Jun., 2024] [arXiv, 2024]
[Paper] [GitHub]
DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
Bowen Song, Jason Hu, Zhaoxu Luo, Jeffrey A. Fessler, Liyue Shen
[14th Jun., 2024] [arXiv, 2024]
[Paper]
Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
Jason Hu, Bowen Song, Xiaojian Xu, Liyue Shen, Jeffrey A. Fessler
[4th Jun., 2024] [arXiv, 2024]
[Paper]
DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised $h$-transform
Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio
[3rd Jun., 2024] [arXiv, 2024]
[Paper]
Blaze3DM: Marry Triplane Representation with Diffusion for 3D Medical Inverse Problem Solving
Jia He, Bonan Li, Ge Yang, Ziwen Liu
[24th May, 2024] [arXiv, 2024]
[Paper]
Reducing the cost of posterior sampling in linear inverse problems via task-dependent score learning
Fabian Schneider, Duc-Lam Duong, Matti Lassas, Maarten V. de Hoop, Tapio Helin
[24th May, 2024] [arXiv, 2024]
[Paper] [GitHub]
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
Mengxiao Geng, Jiahao Zhu, Xiaolin Zhu, Qiqing Liu, Dong Liang, Qiegen Liu
[9th May, 2024] [arXiv, 2024]
[Paper] [GitHub]
Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data
Alexander Denker, Zeljko Kereta, Imraj Singh, Tom Freudenberg, Tobias Kluth, Peter Maass, Simon Arridge
[6th May, 2024] [Applied Mathematics for Modern Challenges Journal, 2024]
[Paper] [[GitHub] \ (https://github.com/alexdenker/eit_fenicsx)]
Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems
Hossein Askari, Fred Roosta, Hongfu Sun
[4th Apr., 2024] [arXiv, 2024]
[Paper]
Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction
Sreemanti Dey, Snigdha Saha, Berthy T. Feng, Manxiu Cui, Laure Delisle, Oscar Leong, Lihong V. Wang, Katherine L. Bouman
[30th Mar., 2024] [arXiv, 2024]
[Paper]
U2MRPD: Unsupervised undersampled MRI reconstruction by prompting a large latent diffusion model
Ziqi Gao, S. Kevin Zhou
[16th Feb., 2024] [arXiv, 2024]
[Paper] [GitHub]
Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model
Matthew A. Chan, Maria J. Molina, Christopher A. Metzler
[5th Feb., 2024] [arXiv, 2024]
[Paper]
A Comparative Study of Variational Autoencoders, Normalizing Flows, and Score-based Diffusion Models for Electrical Impedance Tomography
Huihui Wang, Guixian Xu, Qingping Zhou
[29th Nov., 2023] [Journal of Inverse and Ill-posed Problems, 2024]
[Paper] [Github]
Ultrasound image reconstruction with denoising diffusion restoration models
Yuxin Zhang, Clément Huneau, Jérôme Idier, Diana Mateus
[8th Oct., 2023] [DGM4MICCAI, 2023]
[Paper] [Github]
Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction
Shijun Liang, Ismail Alkhouri, Qing Qu, Rongrong Wang, Saiprasad Ravishankar
[06th Oct., 2024] [ICASSP, 2025]
[Paper]
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae Hoon Roh, Simon Arridge, Peter Maass, Bangti Jin, Jong Chul Ye
[28th Aug., 2023] [IEEE TMI, 2023]
[Paper]
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
Muhammad U. Mirza, Onat Dalmaz, Hasan A. Bedel, Gokberk Elmas, Yilmaz Korkmaz, Alper Gungor, Salman UH Dar, Tolga Çukur
[4th Aug., 2023] [arXiv, 2023]
[Paper] [Github]
Fast and Stable Diffusion Inverse Solver with History Gradient Update
Linchao He, Hongyu Yan, Mengting Luo, Hongjie Wu, Kunming Luo, Wang Wang, Wenchao Du, Hu Chen, Hongyu Yang, Yi Zhang, Jiancheng Lv
[22th Jul., 2023] [arXiv, 2023]
[Paper]
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen
[16th Jul., 2023] [arXiv, 2023]
[Paper]
DiffuseIR: Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images
Mingjie Pan, Yulu Gan, Fangxu Zhou, Jiaming Liu, Aimin Wang, Shanghang Zhang, Dawei Li
[21st Jun., 2023] [arXiv, 2023]
[Paper]
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Sriram Ravula, Brett Levac, Ajil Jalal, Jonathan I. Tamir, Alexandros G. Dimakis
[5th Jun., 2023] [arXiv, 2023]
[Paper] [Github]
Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data
Asad Aali, Marius Arvinte, Sidharth Kumar, Jonathan I. Tamir
[2nd May, 2023] [arXiv, 2023]
[Paper]
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
Zhuo-Xu Cui, Chentao Cao, Jing Cheng, Sen Jia, Hairong Zheng, Dong Liang, Yanjie Zhu
[11th Apr., 2023] [arXiv, 2023]
[Paper]
Sub-volume-based Denoising Diffusion Probabilistic Model for Cone-beam CT Reconstruction from Incomplete Data
Wenjun Xia, Chuang Niu, Wenxiang Cong, Ge Wang
[22nd Mar., 2023] [arXiv, 2023]
[Paper]
Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models
Suhyeon Lee, Hyungjin Chung, Minyoung Park, Jonghyuk Park, Wi-Sun Ryu, Jong Chul Ye
[15th Mar., 2023] [arXiv, 2023]
[Paper]
Fast Diffusion Sampler for Inverse Problems by Geometric Decomposition
Hyungjin Chung, Suhyeon Lee, Jong Chul Ye
[10th Mar., 2023] [arXiv, 2023]
[Paper]
Diffusion Denoising for Low-Dose-CT Model
Runyi Li
[27th Jan., 2023] [arXiv, 2023]
[Paper]
Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction
Gyutaek Oh, Jeong Eun Lee, Jong Chul Ye
[8th Jan., 2023] [arXiv, 2023]
[Paper]
Universal Generative Modeling in Dual-domain for Dynamic MR Imaging
Chuanming Yu, Yu Guan, Ziwen Ke, Dong Liang, Qiegen Liu
[15th Dec., 2022] [arXiv, 2022]
[Paper]
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Chentao Cao, Zhuo-Xu Cui, Jing Cheng, Sen Jia, Hairong Zheng, Dong Liang, Yanjie Zhu
[14th Dec., 2022] [arXiv, 2022]
[Paper]
One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
Bin Huang, Liu Zhang, Shiyu Lu, Boyu Lin, Weiwen Wu, Qiegen Liu
[7th Dec., 2022] [arXiv, 2022]
[Paper]
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Stewart He, K. Aditya Mohan, Ulugbek S. Kamilov, Hyojin Kim
[22nd Nov., 2022] [arXiv, 2022]
[Paper]
Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models
Hyungjin Chung, Dohoon Ryu, Michael T. McCann, Marc L. Klasky, Jong Chul Ye
[19th Nov., 2022] [arXiv, 2022]
[Paper] [Github]
Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
Wenjun Xia, Wenxiang Cong, Ge Wang
[18th Nov., 2022] [arXiv, 2022]
[Paper]
Accelerated Motion Correction for MRI using Score-Based Generative Models
Brett Levac, Ajil Jalal, Jonathan I. Tamir
[1st Nov., 2022] [arXiv, 2022]
[Paper]
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction
Zhuo-Xu Cui, Chentao Cao, Shaonan Liu, Qingyong Zhu, Jing Cheng, Haifeng Wang, Yanjie Zhu, Dong Liang
[2nd Sep., 2022] [IEEE TMI, 2022]
[Paper]
One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
Hong Peng, Chen Jiang, Jing Cheng, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu
[15th Aug., 2022] [arXiv, 2022]
[Paper] [Github]
High-Frequency Space Diffusion Models for Accelerated MRI
Chentao Cao, Zhuo-Xu Cui, Shaonan Liu, Dong Liang, Yanjie Zhu
[10th Aug., 2022] [arXiv, 2022]
[Paper]
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
Salman UH Dar, Şaban Öztürk, Yilmaz Korkmaz, Gokberk Elmas, Muzaffer Özbey, Alper Güngör, Tolga Çukur
[12th Jul., 2022] [arXiv, 2022]
[Paper]
Improving Diffusion Models for Inverse Problems using Manifold Constraints
Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, Jong Chul Ye
[2nd Jun., 2022] [NeurIPS, 2022]
[Paper]
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Zongjiang Tu, Die Liu, Xiaoqing Wang, Chen Jiang, Pengwen Zhu, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu
[8th May, 2022] [arXiv, 2022]
[Paper] [Github]
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Cheng Peng, Pengfei Guo, S. Kevin Zhou, Vishal Patel, Rama Chellappa
[8th Mar., 2022] [MICCAI, 2022]
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction
Yutong Xie, Quanzheng Li
[5th Mar., 2022] [MICCAI, 2022]
MRI Reconstruction via Data Driven Markov Chain with Joint Uncertainty Estimation
Guanxiong Luo, Martin Heide, Martin Uecker
[3rd Feb., 2022] [arXiv, 2022]
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction
Hyungjin Chung, Byeongsu Sim, Jong Chul Ye
[9th Dec., 2021] [CVPR, 2021]
[Paper]
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
Yang Song, Liyue Shen, Lei Xing, Stefano Ermon
[15th Nov., 2021] [ICLR, 2022]
Score-based diffusion models for accelerated MRI
Hyungjin Chung, Jong chul Ye
[8th Oct., 2021] [MIA, 2021]
Robust Compressed Sensing MRI with Deep Generative Priors
Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alexandros G. Dimakis, Jonathan I. Tamir
[3rd Aug., 2021] [NeurIPS, 2021]
Image Generation
Lung-DDPM+: Efficient Thoracic CT Image Synthesis using Diffusion Probabilistic Model
Yifan Jiang, Ahmad Shariftabrizi, Venkata SK. Manem
[12th Aug., 2025] [arXiv, 2025]
[Paper]
Perceptual Evaluation of GANs and Diffusion Models for Generating X-rays
Gregory Schuit, Denis Parra, Cecilia Besa
[10th Aug., 2025] [arXiv, 2025]
[Paper] [GitHub]
MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss
Can Zhao, Pengfei Guo, Dong Yang, Yucheng Tang, Yufan He, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, Daguang Xu
[07th Aug., 2025] [arXiv, 2025]
[Paper] [GitHub] [Demo]
Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis
Kunpeng Qiu, Zhiying Zhou, Yongxin Guo
[31th Jul., 2025] [arXiv, 2025]
[Paper] [GitHub]
LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
Jamil Fayyad, Nourhan Bayasi, Ziyang Yu, Homayoun Najjaran
[30th Jul., 2025] [arXiv, 2025]
[Paper] [GitHub]
Pyramid Hierarchical Masked Diffusion Model for Imaging Synthesis
Xiaojiao Xiao, Qinmin Vivian Hu, Guanghui Wang
[22th Jul., 2025] [arXiv, 2025]
[Paper] [GitHub]
AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
Jan Carreras Boada, Rao Muhammad Umer, Carsten Marr
[07th Jul., 2025] [arXiv, 2025]
[Paper]
MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis
Jianhao Xie, Ziang Zhang, Zhenyu Weng, Yuesheng Zhu, Guibo Luo
[01st Jul., 2025] [arXiv, 2025]
[Paper] [GitHub]
TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency
Minye Shao, Xingyu Miao, Haoran Duan, Zeyu Wang, Jingkun Chen, Yawen Huang, Xian Wu, Jingjing Deng, Yang Long, Yefeng Zheng
[01st Jul., 2025] [arXiv, 2025]
[Paper] [GitHub]
Towards 3D Semantic Image Synthesis for Medical Imaging
Wenwu Tang, Khaled Seyam, Bin Yang
[30th Jun., 2025] [arXiv, 2025]
[Paper]
Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation
Zhifeng Wang, Renjiao Yi, Xin Wen, Chenyang Zhu, Kai Xu, Kunlun He
[24th Jun., 2025] [arXiv, 2025]
[Paper] [GitHub]
Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Janet Wang, Yunbei Zhang, Zhengming Ding, Jihun Hamm
[14th Jun., 2025] [arXiv, 2025]
[Paper] [GitHub]
Prompt-Guided Latent Diffusion with Predictive Class Conditioning for 3D Prostate MRI Generation
Emerson P. Grabke, Masoom A. Haider, Babak Taati
[11th Jun., 2025] [arXiv, 2025]
[Paper] [GitHub]
Skeleton-Guided Diffusion Model for Accurate Foot X-ray Synthesis in Hallux Valgus Diagnosis
Midi Wan, Pengfei Li, Yizhuo Liang, Di Wu, Yushan Pan, Guangzhen Zhu, Hao Wang
[13th May, 2025] [arXiv, 2025]
[Paper] https://github.com/midisec/SCCDM
Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
Kunpeng Qiu, Zhiqiang Gao, Zhiying Zhou, Mingjie Sun, Yongxin Guo
[09th May, 2025] [arXiv, 2025]
[Paper] [GitHub]
ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis
Onkar Susladkar, Gayatri Deshmukh, Yalcin Tur, Gorkhem Durak, Ulas Bagci
[08th May, 2025] [arXiv, 2025]
[Paper] [GitHub]
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models
Karan Jain, Mohammad Nayeem Teli
[15th Apr., 2025] [arXiv, 2025]
[Paper]
MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs
Jiawei Mao, Yuhan Wang, Yucheng Tang, Daguang Xu, Kang Wang, Yang Yang, Zongwei Zhou, Yuyin Zhou
[09th Apr., 2025] [arXiv, 2025]
[Paper] [GitHub] [Project Page]
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing
Yongyi Shi, Ge Wang
[31st Mar., 2025] [arXiv, 2025]
[Paper]
Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation
Zahra TehraniNasab, Amar Kumar, Tal Arbel
[30th Mar., 2025] [MIV-CVPR Workshop, 2025]
[Paper] [GitHub] [Project Page]
CoSimGen: Controllable Diffusion Model for Simultaneous Image and Mask Generation
Rupak Bose, Chinedu Innocent Nwoye, Aditya Bhat, Nicolas Padoy
[25th Mar., 2025] [arXiv, 2025]
[Paper] [Project Page]
ZECO: ZeroFusion Guided 3D MRI Conditional Generation
Feiran Wang, Bin Duan, Jiachen Tao, Nikhil Sharma, Dawen Cai, Yan Yan
[24th Mar., 2025] [arXiv, 2025]
[Paper] [GitHub] [Project Page]
MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis
Marvin Seyfarth, Salman Ul Hassan Dar, Isabelle Ayx, Matthias Alexander Fink, Stefan O. Schoenberg, Hans-Ulrich Kauczor, Sandy Engelhardt
[17th Mar., 2025] [arXiv, 2025]
[Paper]
FCaS: Fine-grained Cardiac Image Synthesis based on 3D Template Conditional Diffusion Model
Jiahao Xia, Yutao Hu, Yaolei Qi, Zhenliang Li, Wenqi Shao, Junjun He, Ying Fu, Longjiang Zhang, Guanyu Yang
[12th Mar., 2025] [arXiv, 2025]
[Paper]
Synthetic Lung X-ray Generation through Cross-Attention and Affinity Transformation
Ruochen Pi, Lianlei Shan
[10th Mar., 2025] [arXiv, 2025]
[Paper]
Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation
Pengchen Liang, Haishan Huang, Bin Pu, Jianguo Chen, Xiang Hua, Jing Zhang, Weibo Ma, Zhuangzhuang Chen, Yiwei Li, Qing Chang
[10th Mar., 2025] [arXiv, 2025]
[Paper]
CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI
Mominul Islam, Mohammad Junayed Hasan, M. R. C. Mahdy
[04th Mar., 2025] [arXiv, 2025]
[Paper] [GitHub]
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality
Milad Yazdani, Yasamin Medghalchi, Pooria Ashrafian, Ilker Hacihaliloglu, Dena Shahriari
[01st Mar., 2025] [MICCAI, 2025]
[Paper] [GitHub]
Advancing AI-Powered Medical Image Synthesis: Insights from MedVQA-GI Challenge Using CLIP, Fine-Tuned Stable Diffusion, and Dream-Booth + LoRA
Ojonugwa Oluwafemi Ejiga Peter, Md Mahmudur Rahman, Fahmi Khalifa
[28th Feb., 2025] [CEUR workshop, 2025]
[Paper]
Robust Polyp Detection and Diagnosis through Compositional Prompt-Guided Diffusion Models
Jia Yu, Yan Zhu, Peiyao Fu, Tianyi Chen, Junbo Huang, Quanlin Li, Pinghong Zhou, Zhihua Wang, Fei Wu, Shuo Wang, Xian Yang
[25th Feb., 2025] [IEEE TMI, 2025]
[Paper] [GitHub]
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
Yifan Jiang, Yannick Lemaréchal, Sophie Plante, Josée Bafaro, Jessica Abi-Rjeile, Philippe Joubert, Philippe Després, Venkata Manem
[21st Feb., 2025] [arXiv, 2025]
[Paper] [GitHub]
Ultrasound Image Generation using Latent Diffusion Models
Benoit Freiche, Anthony El-Khoury, Ali Nasiri-Sarvi, Mahdi S. Hosseini, Damien Garcia, Adrian Basarab, Mathieu Boily, Hassan Rivaz
[12th Feb., 2025] [arXiv, 2025]
[Paper] https://github.com/Toterino/US-image-generation
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data
Xichen Xu, Wentao Chen, Weimin Zhou
[31th Jan., 2025] [arXiv, 2025]
[Paper]
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling
Iman Khazrak, Shakhnoza Takhirova, Mostafa M. Rezaee, Mehrdad Yadollahi, Robert C. Green II, Shuteng Niu
[17th Dec., 2024] [arXiv, 2024]
[Paper]
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation
Haoshen Wang, Zhentao Liu, Kaicong Sun, Xiaodong Wang, Dinggang Shen, Zhiming Cui
[17th Dec., 2024] [arXiv, 2024]
[Paper]
MRGen: Segmentation Data Engine for Underrepresented MRI Modalities
Haoning Wu, Ziheng Zhao, Ya Zhang, Yanfeng Wang, Weidi Xie
[04th Dec., 2024] [ICCV, 2025]
[Paper] [GitHub] [Project Page]
Volumetric Conditioning Module to Control Pretrained Diffusion Models for 3D Medical Images
Suhyun Ahn, Wonjung Park, Jihoon Cho, Seunghyuck Park, Jinah Park
[29th Oct., 2024] [arXiv, 2024]
[Paper] [GitHub]
Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
Eva Prakash, Jeya Maria Jose Valanarasu, Zhihong Chen, Eduardo Pontes Reis, Andrew Johnston, Anuj Pareek, Christian Bluethgen, Sergios Gatidis, Cameron Olsen, Akshay Chaudhari, Andrew Ng, Curtis Langlotz
[27th Nov., 2024] [arXiv, 2024]
[Paper]
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis
Paul Friedrich, Alicia Durrer, Julia Wolleb, Philippe C. Cattin
[26th Nov., 2024] [BraTS, 2024]
[Paper] [GitHub]
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models
Pedro Morão, Joao Santinha, Yasna Forghani, Nuno Loução, Pedro Gouveia, Mario A. T. Figueiredo
[31th Oct., 2024] [arXiv, 2024]
[Paper] [GitHub]
Deep Generative Models for 3D Medical Image Synthesis
Paul Friedrich, Yannik Frisch, Philippe C. Cattin
[23th Oct., 2024] [arXiv, 2024]
[Paper]
Synthetic Augmentation for Anatomical Landmark Localization using DDPMs
Arnela Hadzic, Lea Bogensperger, Simon Johannes Joham, Martin Urschler
[16th Oct., 2024] [SASHIMI, 2024]
[Paper]
Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation
Kathrin Khadra, Utku Türkbey
[16th Oct., 2024] [arXiv, 2024]
[Paper]
Multiscale Latent Diffusion Model for Enhanced Feature Extraction from Medical Images
Rabeya Tus Sadia, Jie Zhang, Jin Chen
[05th Oct., 2024] [arXiv, 2024]
[Paper]
Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation
Yuran Wang, Zhijing Wan, Yansheng Qiu, Zheng Wang
[30th Sep., 2024] [arXiv, 2024]
[Paper]
Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification \
Xinrui Zhou, Yuhao Huang, Haoran Dou, Shijing Chen, Ao Chang, Jia Liu, Weiran Long, Jian Zheng, Erjiao Xu, Jie Ren, Ruobing Huang, Jun Cheng, Wufeng Xue, Dong Ni
[25th Sep., 2024] [arXiv, 2024]
[Paper]
Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis
Sven Lüpke, Yousef Yeganeh, Ehsan Adeli, Nassir Navab, Azade Farshad
[20th Sep., 2024] [arXiv, 2024]
[Paper]
MAISI: Medical AI for Synthetic Imaging
Pengfei Guo, Can Zhao, Dong Yang, Ziyue Xu, Vishwesh Nath, Yucheng Tang, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, Daguang Xu
[13th Sep., 2024] [arXiv, 2024]
[Paper]
Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records
Daeun Kyung, Junu Kim, Tackeun Kim, Edward Choi
[11th Sep., 2024] [arXiv, 2024]
[Paper]
Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques
Davide Clode da Silva, Marina Musse Bernardes, Nathalia Giacomini Ceretta, Gabriel Vaz de Souza, Gabriel Fonseca Silva, Rafael Heitor Bordini, Soraia Raupp Musse
[6th Sep., 2024] [arXiv, 2024]
[Paper]
Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis
Arjun Krishna, Ge Wang, Klaus Mueller
[6th Sep., 2024] [arXiv, 2024]
[Paper]
LPUWF-LDM: Enhanced Latent Diffusion Model for Precise Late-phase UWF-FA Generation on Limited Dataset
Zhaojie Fang, Xiao Yu, Guanyu Zhou, Ke Zhuang, Yifei Chen, Ruiquan Ge, Changmiao Wang, Gangyong Jia, Qing Wu, Juan Ye, Maimaiti Nuliqiman, Peifang Xu, Ahmed Elazab
[1st Sep., 2024] [arXiv, 2024]
[Paper] [GitHub]
Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis
Van Phi Nguyen, Tri Nhan Luong Ha, Huy Hieu Pham, Quoc Long Tran
[6th Aug., 2024] [MICCAI, 2024]
[Paper] [GitHub]
Mpox Detection Advanced: Rapid Epidemic Response Through Synthetic Data
Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya, Prarththanan Sothyrajah, Thanveer Ahamed, Dinuka Wijesundara
[25th Jul., 2024] [arXiv, 2024]
[Paper]
URCDM: Ultra-Resolution Image Synthesis in Histopathology
Sarah Cechnicka, James Ball, Matthew Baugh, Hadrien Reynaud, Naomi Simmonds, Andrew P. T. Smith, Catherine Horsfield, Candice Roufosse, Bernhard Kainz
[18th Jul., 2024] [MICCAI, 2024]
[Paper] [GitHub]
CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation
Weiheng Yao, Shuqiang Wang
[16th Jul., 2024] [arXiv, 2024]
[Paper]
Bora: Biomedical Generalist Video Generation Model
Weixiang Sun, Xiaocao You, Ruizhe Zheng, Zhengqing Yuan, Xiang Li, Lifang He, Quanzheng Li, Lichao Sun
[11th Jul., 2024] [arXiv, 2024]
[Paper] [GitHub]
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough? Adrian Tormos, Blanca Llauradó, Fernando Núñez, Axel Romero, Dario Garcia-Gasulla, Javier Béjar [28th Nov., 2024] [arXiv, 2024] [Paper]
Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge
Lemuel Puglisi, Daniel C. Alexander, Daniele Ravì
[22th Aug., 2024] [MICCAI, 2024]
[Paper] [GitHub]
SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesis
Yuchen Mao, Hongwei Li, Wei Pang, Giorgos Papanastasiou, Guang Yang, Chengjia Wang
[13th Aug., 2024] [arXiv, 2024]
[Paper]
Temporal Evolution of Knee Osteoarthritis: A Diffusion-based Morphing Model for X-ray Medical Image Synthesis
Zhe Wang, Aladine Chetouani, Rachid Jennane, Yuhua Ru, Wasim Issa, Mohamed Jarraya
[1st Aug., 2024] [arXiv, 2024]
[Paper]
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
Deniz Daum, Richard Osuala, Anneliese Riess, Georgios Kaissis, Julia A. Schnabel, Maxime Di Folco
[23rd Jul., 2024] [MICCAI, 2024]
[Paper] [GitHub]
Non-Reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs
Karl Van Eeden Risager, Torkan Gholamalizadeh, Mostafa Mehdipour Ghazi
[20th Jul., 2024] [arXiv, 2024]
[Paper]
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training
Guillermo Jimenez-Perez, Pedro Osorio, Josef Cersovsky, Javier Montalt-Tordera, Jens Hooge, Steffen Vogler, Sadegh Mohammadi
[16th Jul., 2024] [arXiv, 2024]
[Paper]
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
Jian-Qing Zheng, Yuanhan Mo, Yang Sun, Jiahua Li, Fuping Wu, Ziyang Wang, Tonia Vincent, Bartłomiej W. Papież
[9th Jul., 2024] [arXiv, 2024]
[Paper] [GitHub]
RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features
Inye Na, Jonghun Kim, Eun Sook Ko, Hyunjin Park
[8th, Jul., 2024] [MICCAI, 2024]
[Paper] [GitHub]
Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation
Marina Domínguez, Yordanka Velikova, Nassir Navab, Mohammad Farid Azampour
[7th Jul., 2024] [IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control Journal, 2024]
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Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model
Sepehr Salem Ghahfarokhi, Tyrell To, Julie Jorns, Tina Yen, Bing Yu, Dong Hye Ye
[1st Jul., 2024] [ISBI, 2024]
[Paper]
From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysis
Janet Wang, Yunsung Chung, Zhengming Ding, Jihun Hamm
[26th Jun., 2024] [arXiv, 2024]
[Paper]
Towards Synchronous Memorizability and Generalizability with Site-Modulated Diffusion Replay for Cross-Site Continual Segmentation
Dunyuan Xu, Xi Wang, Jingyang Zhang, Pheng-Ann Heng
[25th Jun., 2024] [arXiv, 2024]
[Paper] [GitHub]
Test-Time Generative Augmentation for Medical Image Segmentation
Xiao Ma, Yuhui Tao, Yuhan Zhang, Zexuan Ji, Yizhe Zhang, Qiang Chen
[25th Jun., 2024] [arXiv, 2024]
[Paper] [GitHub]
X-ray2CTPA: Generating 3D CTPA scans from 2D X-ray conditioning
Noa Cahan, Eyal Klang, Galit Aviram, Yiftach Barash, Eli Konen, Raja Giryes, Hayit Greenspan
[23rd Jun., 2024] [arXiv, 2024]
[Paper] [GitHub]
Image Distillation for Safe Data Sharing in Histopathology
Zhe Li, Bernhard Kainz
[19th Jun., 2024] [arXiv, 2024]
[Paper] [GitHub]
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan
[8th Jun., 2024] [arXiv, 2024]
[Paper] [GitHub]
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
Chenxin Li, Xinyu Liu, Wuyang Li, Cheng Wang, Hengyu Liu, Yixuan Yuan
[5th Jun, 2024] [arXiv, 2024]
[Paper] [GitHub] [Website]
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
Hadrien Reynaud, Qingjie Meng, Mischa Dombrowski, Arijit Ghosh, Thomas Day, Alberto Gomez, Paul Leeson, Bernhard Kainz
[2nd Jun, 2024] [MICCAI, 2024]
[Paper] [GitHub]
Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models
Cristiano Patrício, Carlo Alberto Barbano, Attilio Fiandrotti, Riccardo Renzulli, Marco Grangetto, Luis F. Teixeira, João C. Neves
[2nd Jun, 2024] [arXiv, 2024]
[Paper] [GitHub]
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification
Hansang Lee, Haeil Lee, Helen Hong
[31st May, 2024] [arXiv, 2024]
[Paper]
Memory-efficient High-resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Models
Kun Huang, Xiao Ma, Yuhan Zhang, Na Su, Songtao Yuan, Yong Liu, Qiang Chen, Huazhu Fu
[26th May, 2024] [arXiv, 2024]
[Paper] [GitHub]
MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis
Joseph Cho, Cyril Zakka, Rohan Shad, Ross Wightman, Akshay Chaudhari, William Hiesinger
[16th May, 2024] [arXiv, 2024]
[Paper]
VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis
Zhihan Ju, Wanting Zhou
[09th May, 2024] [arXiv, 2024]
[Paper]
Long Tail Image Generation Through Feature Space Augmentation and Iterated Learning
Rafael Elberg, Denis Parra, Mircea Petrache
[2nd May, 2024] [arXiv, 2024]
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Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery
Payal Varshney, Adriano Lucieri, Christoph Balada, Andreas Dengel, Sheraz Ahmed
[16th Apr., 2024] [arXiv, 2024]
[Paper]
MaSkel: A Model for Human Whole-body X-rays Generation from Human Masking Images
Yingjie Xi, Boyuan Cheng, Jingyao Cai, Jian Jun Zhang, Xiaosong Yang
[13th Apr., 2024] [arXiv, 2024]
[Paper] [GitHub]
Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models
Siyuan Mei, Fuxin Fan, Fabian Wagner, Mareike Thies, Mingxuan Gu, Yipeng Sun, Andreas Maier
[4th Apr., 2024] [arXiv, 2024]
[Paper]
Debiasing Cardiac Imaging with Controlled Latent Diffusion Models
Grzegorz Skorupko, Richard Osuala, Zuzanna Szafranowska, Kaisar Kushibar, Nay Aung, Steffen E Petersen, Karim Lekadir, Polyxeni Gkontra
[28th Mar., 2024] [arXiv, 2024]
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Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
Pooria Ashrafian, Milad Yazdani, Moein Heidari, Dena Shahriari, Ilker Hacihaliloglu
[28th Mar., 2024] [arXiv, 2024]
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CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation
Yongrui Yu, Hanyu Chen, Zitian Zhang, Qiong Xiao, Wenhui Lei, Linrui Dai, Yu Fu, Hui Tan, Guan Wang, Peng Gao, Xiaofan Zhang
[26th Mar., 2024] [arXiv, 2024]
[Paper]
Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models
Rowan Bradbury, Katherine A. Vallis, Bartlomiej W. Papiez
[26th Mar., 2024] [arXiv, 2024]
[Paper]
MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipeline
Yasamin Medghalchi, Niloufar Zakariaei, Arman Rahmim, Ilker Hacihaliloglu
[25th Mar., 2024] [arXiv, 2024]
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LeFusion: Synthesizing Myocardial Pathology on Cardiac MRI via Lesion-Focus Diffusion Models
Hantao Zhang, Jiancheng Yang, Shouhong Wan, Pascal Fua
[21st Mar., 2024] [arXiv, 2024]
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Generative Enhancement for 3D Medical Images
Lingting Zhu, Noel Codella, Dongdong Chen, Zhenchao Jin, Lu Yuan, Lequan Yu
[14th Mar., 2024] [arXiv, 2024]
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XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Model
Anees Ur Rehman Hashmi, Ibrahim Almakky, Mohammad Areeb Qazi, Santosh Sanjeev, Vijay Ram Papineni, Dwarikanath Mahapatra, Mohammad Yaqub
[14th Mar., 2024] [arXiv, 2024]
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TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields
Ashish Sinha, Ghassan Hamarneh
[13th Mar., 2024] [MICCAI, 2024]
[Paper] [Github]
Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields
Ashish Sinha, Ghassan Hamarneh
[13th Mar., 2024] [arXiv, 2024]
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Federated Data Model
Xiao Chen, Shunan Zhang, Eric Z. Chen, Yikang Liu, Lin Zhao, Terrence Chen, Shanhui Sun
[13th Mar., 2024] [arXiv, 2024]
[Paper]
GuideGen: A Text-Guided Framework for Full-torso Anatomy and CT Volume Generation
Linrui Dai, Rongzhao Zhang, Yongrui Yu, Xiaofan Zhang
[12th Mar., 2024] [arXiv, 2024]
[Paper]
A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images
Cristiana Tiago, Sten Roar Snare, Jurica Sprem, Kristin McLeod
[7th Mar., 2024] [arXiv, 2024]
[Paper]
MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant
Chenlu Zhan, Yu Lin, Gaoang Wang, Hongwei Wang, Jian Wu
[7th Mar., 2024] [CVPR, 2024]
[Paper]
An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels
Shumpei Takezaki, Seiichi Uchida
[1st Mar., 2024] [ISBI, 2024]
[Paper]
Structure Preserving Diffusion Models
Haoye Lu, Spencer Szabados, Yaoliang Yu
[29th Feb., 2024] [arXiv, 2024]
[Paper]
Towards Generalizable Tumor Synthesis
Qi Chen, Xiaoxi Chen, Haorui Song, Zhiwei Xiong, Alan Yuille, Chen Wei, Zongwei Zhou
[29th Feb., 2024] [CVPR, 2024]
[Paper] [Github]
WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
Paul Friedrich, Julia Wolleb, Florentin Bieder, Alicia Durrer, Philippe C. Cattin
[29th Feb., 2024] [DGM4MICCAI, 2024]
[Paper] [Github] [Project Page]
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
Nicholas Konz, Yuwen Chen, Haoyu Dong, Maciej A. Mazurowski
[7th Feb., 2024] [arXiv, 2024]
[Paper]
SynthVision -- Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data
Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya, Thanveer Ahamed, Dinuka Wijesundara, Prarththanan Sothyrajah
[5th Jan., 2024] [arXiv, 2024]
[Paper]
DDPM based X-ray Image Synthesizer
Praveen Mahaulpatha, Thulana Abeywardane, Tomson George
[3rd Jan., 2024] [arXiv, 2024]
[Paper]
Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
Nina Weng, Paraskevas Pegios, Aasa Feragen, Eike Petersen, Siavash Bigdeli
[21st Dec., 2023] [arXiv, 2023]
[Paper]
On the existence of optimal multi-valued decoders and their accuracy bounds for undersampled inverse problems
Fangxin Shang, Jie Fu, Yehui Yang, Haifeng Huang, Junwei Liu, Lei Ma
[1st Dec., 2023] [arXiv, 2023]
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Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
Ye Lin Tun, Chu Myaet Thwal, Ji Su Yoon, Sun Moo Kang, Chaoning Zhang, Choong Seon Hong
[28th Nov., 2023] [arXiv, 2023]
[Paper]
Overcoming Pathology Image Data Deficiency: Generating Images from Pathological Transformation Process
Zeyu Liu, Yufang He, Yu Zhao, Yunlu Feng, Guanglei Zhang
[21st Nov., 2023] [arXiv, 2023]
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MAM-E: Mammographic synthetic image generation with diffusion models
Ricardo Montoya-del-Angel, Karla Sam-Millan, Joan C Vilanova, Robert Martí
[16th Nov., 2023] [arXiv, 2023]
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Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
Bardia Khosravi, Frank Li, Theo Dapamede, Pouria Rouzrokh, Cooper U. Gamble, Hari M. Trivedi, Cody C. Wyles, Andrew B. Sellergren, Saptarshi Purkayastha, Bradley J. Erickson, Judy W. Gichoya
[15th Nov., 2023] [arXiv, 2023]
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Synthesizing Diabetic Foot Ulcer Images with Diffusion Model
Reza Basiri, Karim Manji, Francois Harton, Alisha Poonja, Milos R. Popovic, Shehroz S. Khan
[31st Oct., 2023] [arXiv, 2023]
[Paper]
MCRAGE: Synthetic Healthcare Data for Fairness
Keira Behal, Jiayi Chen, Caleb Fikes, Sophia Xiao
[27th Oct., 2023] [arXiv, 2023]
[Paper]
Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation
Daniel Saragih, Pascal Tyrrell
[25th Oct., 2023] [arXiv, 2023]
[Paper] [Github]
EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
Zheyuan Zhang, Lanhong Yao, Bin Wang, Debesh Jha, Elif Keles, Alpay Medetalibeyoglu, Ulas Bagci
[19th Oct., 2023] [arXiv, 2023]
[Paper]
Echocardiography video synthesis from end diastolic semantic map via diffusion model
Phi Nguyen Van, Duc Tran Minh, Hieu Pham Huy, Long Tran Quoc
[11th Oct., 2023] [arXiv, 2023]
[Paper]
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Yuan Zhong, Suhan Cui, Jiaqi Wang, Xiaochen Wang, Ziyi Yin, Yaqing Wang, Houping Xiao, Mengdi Huai, Ting Wang, Fenglong Ma
[4th Oct., 2023] [SIAM, 2024]
[Paper]
M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations
Giada Zingarini, Davide Cozzolino, Riccardo Corvi, Giovanni Poggi, Luisa Verdoliva
[3rd Sep., 2023] [MICCAI, 2023]
[Paper] [GitHub]
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models
Yuhao Du, Yuncheng Jiang, Shuangyi Tan, Xusheng Wu, Qi Dou, Zhen Li, Guanbin Li, Xiang Wan
[3rd Sep., 2023] [MICCAI, 2023]
[Paper] [GitHub]
Augmenting medical image classifiers with synthetic data from latent diffusion models
Luke W. Sagers, James A. Diao, Luke Melas-Kyriazi, Matthew Groh, Pranav Rajpurkar, Adewole S. Adamson, Veronica Rotemberg, Roxana Daneshjou, Arjun K. Manrai
[23rd Aug., 2023] [arXiv, 2023]
[Paper]
Synthetic Augmentation with Large-scale Unconditional Pre-training
Jiarong Ye, Haomiao Ni, Peng Jin, Sharon X. Huang, Yuan Xue
[8th Aug., 2023] [MICCAI, 2023]
[Paper] [GitHub]
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
Lingting Zhu, Zeyue Xue, Zhenchao Jin, Xian Liu, Jingzhen He, Ziwei Liu, Lequan Yu
[19th Jul., 2023] [arXiv, 2023]
[Paper]
DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRI
Hasan A. Bedel, Tolga C¸ ukur
[18th Jul., 2023] [arXiv, 2023]
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Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane
Kun Han, Shanlin Sun, Xiaohui Xie
[4th July. 2023] [arXiv, 2023]
[Paper]
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis
Salman Ul Hassan Dar, Arman Ghanaat, Jannik Kahmann, Isabelle Ayx, Theano Papavassiliu, Stefan O. Schoenberg, Sandy Engelhardt
[3rd July. 2023] [arXiv, 2023]
[Paper]
DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets
Hyun-Jic Oh, Won-Ki Jeong
[25th Jun. 2023] [arXiv, 2023]
[Paper]
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
Marco Aversa, Gabriel Nobis, Miriam Hägele, Kai Standvoss, Mihaela Chirica, Roderick Murray-Smith, Ahmed Alaa, Lukas Ruff, Daniela Ivanova, Wojciech Samek, Frederick Klauschen, Bruno Sanguinetti, Luis Oala
[23th Jun. 2023] [arXiv, 2023]
[Paper]
Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback
Shenghuan Sun, Gregory M. Goldgof, Atul Butte, Ahmed M. Alaa
[16th Jun., 2023] [arXiv, 2023]
[Paper]
Diffusion Models for Realistic CT Image Generation
Maialen Stephens Txurio, Karen López-Linares Román, Andrés Marcos-Carrión, Pilar Castellote-Huguet, José M. Santabárbara-Gómez, Iván Macía Oliver, Miguel A. González Ballester
[31th May, 2023] [KES, 2023]
[Paper]
Evaluating the feasibility of using Generative Models to generate Chest X-Ray Data
Muhammad Danyal Malik, Danish Humair
[30th May, 2023] [arXiv, 2023]
[Paper] [Github]
Conditional Diffusion Models for Semantic 3D Medical Image Synthesis
Zolnamar Dorjsembe, Hsing-Kuo Pao, Sodtavilan Odonchimed, Furen Xiao
[29th May, 2023] [arXiv, 2023]
[Paper]
GenerateCT: Text-Guided 3D Chest CT Generation
Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Alperen Tezcan, Ayse Gulnihan Simsek, Furkan Almas, Sevval Nil Esirgun, Hadrien Reynaud, Sarthak Pati, Christian Bluethgen, Bjoern Menze
[25th May, 2023] [arXiv, 2023]
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Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain tumor images
Muhammad Usman Akbar, Wuhao Wang, Anders Eklund
[12th May, 2023] [arXiv, 2023]
[Paper]
Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models
Sojung Go, Younghoon Ji, Sang Jun Park, Soochahn Lee
[11th May, 2023] [arXiv, 2023]
[Paper]
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation
David Stojanovski, Uxio Hermida, Pablo Lamata, Arian Beqiri, Alberto Gomez
[9th May, 2023] [arXiv, 2023]
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Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model
Taofeng Xie, Chentao Cao, Zhuoxu Cui, Yu Guo, Caiying Wu, Xuemei Wang, Qingneng Li, Zhanli Hu, Tao Sun, Ziru Sang, Yihang Zhou, Yanjie Zhu, Dong Liang, Qiyu Jin, Guoqing Chen, Haifeng Wang
[6th May, 2023] [arXiv, 2023]
[Paper]
High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model
Xuan Zhao, Benjamin Hou
[2nd May, 2023] [arXiv, 2023]
[Paper]
Denoising Diffusion Medical Models
Pham Ngoc Huy, Tran Minh Quan
[19th Apr., 2023] [arXiv, 2023]
[Paper]
Mask-conditioned latent diffusion for generating gastrointestinal polyp images
Roman Macháček, Leila Mozaffari, Zahra Sepasdar, Sravanthi Parasa, Pål Halvorsen, Michael A. Riegler, Vajira Thambawita
[11th Apr., 2023] [arXiv, 2023]
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MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation
Kun Han, Yifeng Xiong, Chenyu You, Pooya Khosravi, Shanlin Sun, Xiangyi Yan, James Duncan, Xiaohui Xie
[8th Apr., 2023] [arXiv, 2023]
[Paper]
Towards Realistic Ultrasound Fetal Brain Imaging Synthesis
Michelle Iskandar, Harvey Mannering, Zhanxiang Sun, Jacqueline Matthew, Hamideh Kerdegari, Laura Peralta, Miguel Xochicale
[8th Apr., 2023] [arXiv, 2023]
[Paper] [Github]
2D Medical Image Synthesis Using Transformer-based Denoising Diffusion Probabilistic Model
Shaoyan Pan, Tonghe Wang, Richard L J Qiu, Marian Axente, Chih-Wei Chang, Junbo Peng, Ashish B Patel, Joseph Shelton, Sagar A Patel, Justin Roper
[4th Apr., 2023] [Physics in Medicine & Biology, 2023]
[Paper]
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
Xuan Xu, Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna
[3rd Apr., 2023] [arXiv, 2023]
[Paper]
DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding
Xiaoyue Li, Kai Shang, Gaoang Wang, Mark D. Butala
[28th Mar., 2023] [arXiv, 2023]
[Paper]
CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis
Lan Jiang, Ye Mao, Xi Chen, Xiangfeng Wang, Chao Li
[24th Mar., 2023] [arXiv, 2023]
[Paper]
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
Hadrien Reynaud, Mengyun Qiao, Mischa Dombrowski, Thomas Day, Reza Razavi, Alberto Gomez, Paul Leeson, Bernhard Kainz
[22nd Mar., 2023] [arXiv, 2023]
[Paper] [Github]
NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models
Aman Shrivastava, P. Thomas Fletcher
[20th Mar., 2023] [arXiv, 2023]
[Paper]
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
[20th Mar., 2023] [arXiv, 2023]
[Paper]
Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection
Nikhil J. Dhinagar, Sophia I. Thomopoulos, Emily Laltoo, Paul M. Thompson
[14th Mar., 2023] [arXiv, 2023]
[Paper]
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Yuanzhi Zhu, Jiangshe Zhang, Shuang Xu, Yulun Zhang, Kai Zhang, Deyu Meng, Radu Timofte, Luc Van Gool
[13th Mar., 2023] [arXiv, 2023]
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