Awesome Diffusion Models In Medical Imaging Save
Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)
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
- Fourth release: Coming soon!
- 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
Survey Papers
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]
Papers
Anomaly Detection
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]
[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]
[Paper] [Github]
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]
[Paper] [Github]
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection
Evi M.C. Huijben, Sina Amirrajab, Josien P.W. Pluim
[12th Oct., 2023] [arXiv, 2023]
[Paper]
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]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper] [Github]
Denoising
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
Analysing Diffusion Segmentation for Medical Images
Mathias Öttl, Siyuan Mei, Frauke Wilm, Jana Steenpass, Matthias Rübner, Arndt Hartmann, Matthias Beckmann, Peter Fasching, Andreas Maier, Ramona Erber, Katharina Breininger
[21st Mar., 2024] [arXiv, 2024]
[Paper]
Diffusion and Multi-Domain Adaptation Methods for Eosinophil Segmentation
Kevin Lin, Donald Brown, Sana Syed, Adam Greene
[17th Mar., 2024] [arXiv, 2024]
[Paper]
Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation
Zolnamar Dorjsembe, Hsing-Kuo Pao, Furen Xiao
[6th Feb., 2024] [arXiv, 2024]
[Paper] [Github]
Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation
Fahim Ahmed Zaman, Mathews Jacob, Amanda Chang, Kan Liu, Milan Sonka, Xiaodong Wu
[19th Dec., 2023] [arXiv, 2023]
[Paper]
LSegDiff: A Latent Diffusion Model for Medical Image Segmentation
Fahim Ahmed Zaman, Mathews Jacob, Amanda Chang, Kan Liu, Milan Sonka, Xiaodong Wu
[7th Dec., 2023] [SOICT, 2023]
[Paper]
Robust semi-supervised segmentation with timestep ensembling diffusion models
Margherita Rosnati, Melanie Roschewitz, Ben Glocker
[13th Nov., 2023] [arXiv, 2023]
[Paper]
A 3D generative model of pathological multi-modal MR images and segmentations
Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges, Mark S. Graham, Tom Vercauteren, M. Jorge Cardoso
[8th Nov., 2023] [arXiv, 2023]
[Paper] [Github]
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
Haonan Wang, Xiaomeng Li
[17th Oct., 2023] [NeurIPS, 2023]
[Paper] [Github]
Certification of Deep Learning Models for Medical Image Segmentation
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Nikos Paragios, Marie-Pierre Revel, Maria Vakalopoulou
[5th Oct., 2023] [MICCAI, 2023]
[Paper] [Github]
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation
Zhiqing Zhang, Guojia Fan, Tianyong Liu, Nan Li, Yuyang Liu, Ziyu Liu, Canwei Dong, Shoujun Zhou
[12th Aug., 2023] [arXiv, 2023]
[Paper]
A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models
Yunguan Fu, Yiwen Li, Shaheer U Saeed, Matthew J Clarkson, Yipeng Hu
[30th Aug., 2023] [arXiv, 2023]
[Paper] [Github]
Masked Diffusion as Self-supervised Representation Learner
Zixuan Pan, Jianxu Chen, Yiyu Shi
[10th Aug., 2023] [arXiv, 2023]
[Paper]
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
Afshin Bozorgpour, Yousef Sadegheih, Amirhossein Kazerouni, Reza Azad, Dorit Merhof
[5th Aug., 2023] [MICCAI Workshop, 2023]
[Paper] [Github]
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation
Boah Kim, Yujin Oh, Bradford J. Wood, Ronald M. Summers, Jong Chul Ye
[31st Jul., 2023] [arXiv, 2023]
[Paper]
Pre-Training with Diffusion models for Dental Radiography Segmentation
Jérémy Rousseau, Christian Alaka, Emma Covili, Hippolyte Mayard, Laura Misrachi, Willy Au
[26th Jul., 2023] [arXiv, 2023]
[Paper]
FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification
Héctor Carrión, Narges Norouzi
[21st Jul., 2023] [MICCAI, 2023]
[Paper] [Github]
Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models
Tomer Amit, Shmuel Shichrur, Tal Shaharbany, and Lior Wolf
[15th Jun., 2023] [arXiv, 2023]
[Paper] [Github]
Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation
Xinrong Hu, Yu-Jen Chen, Tsung-Yi Ho, Yiyu Shi
[6th Jun., 2023] [arXiv, 2023]
[Paper] [Github]
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models
Muhammad Usman Akbar, Måns Larsson, and Anders Eklund
[5th Jun., 2023] [arXiv, 2023]
[Paper]
Semi-supervised Brain Tumor Segmentation Using Diffusion Models
Ahmed Alshenoudy, Bertram Sabrowsky-Hirsch, Stefan Thumfart, Michael Giretzlehner, Erich Kobler
[1st Jun., 2023] [AIAI, 2023]
[Paper] [Github]
Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model
Fenghe Tang, Jianrui Ding, Lingtao Wang, Min Xian, Chunping Ning
[16th May, 2023] [arXiv, 2023]
[Paper] [Github]
Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features
Nurislam Tursynbek, Marc Niethammer
[28th Apr., 2023] [arXiv, 2023]
[Paper]
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models
Shitong Shao, Xiaohan Yuan, Zhen Huang, Ziming Qiu, Shuai Wang, Kevin Zhou
[26th Apr., 2023] [arXiv, 2023]
[Paper] [Github]
Ambiguous Medical Image Segmentation using Diffusion Models
Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu, Vishal M Patel
[10th Apr., 2023] [CVPR, 2023]
[Paper] [Github]
BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation
Tao Chen, Chenhui Wang, Hongming Shan
[10th Apr., 2023] [arXiv, 2023]
[Paper]
Diffusion Models for Memory-efficient Processing of 3D Medical Images
Florentin Bieder, Julia Wolleb, Alicia Durrer, Robin Sandkühler, Philippe C. Cattin
[27th Mar., 2023] [MIDL, 2023]
[Paper]
Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation
Haipeng Zhou, Lei Zhu, Yuyin Zhou
[22nd Mar., 2023] [arXiv, 2023]
[Paper] [Github]
Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation
Zhaohu Xing, Liang Wan, Huazhu Fu, Guang Yang, Lei Zhu
[18th Mar., 2023] [arXiv, 2023]
[Paper] [Github]
Stochastic Segmentation with Conditional Categorical Diffusion Models
Lukas Zbinden, Lars Doorenbos, Theodoros Pissas, Raphael Sznitman, Pablo Márquez-Neila
[15th Mar., 2023] [ICCV, 2023]
[Paper] [Github]
Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation
Yunguan Fu, Yiwen Li, Shaheer U. Saeed, Matthew J. Clarkson, Yipeng Hu
[10th Mar., 2023] [arXiv, 2023]
[Paper] [Github]
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions
Lea Bogensperger, Dominik Narnhofer, Filip Ilic, Thomas Pock
[10th Mar., 2023] [arXiv, 2023]
[Paper]
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, Yanwu Xu
[19th Jan., 2023] [arXiv, 2023]
[Paper] [Github]
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks
Mathias Öttl, Jana Mönius, Matthias Rübner, Carol I. Geppert, Jingna Qiu, Frauke Wilm, Arndt Hartmann, Matthias W. Beckmann, Peter A. Fasching, Andreas Maier, Ramona Erber, Katharina Breininger
[11th Nov., 2022] [ISBI, 2023]
[Paper]
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Junde Wu, Huihui Fang, Yu Zhang, Yehui Yang, Yanwu Xu
[1st Nov., 2022] [MIDL, 2023]
[Paper] [Github]
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation
Xutao Guo, Yanwu Yang, Chenfei Ye, Shang Lu, Yang Xiang, Ting Ma
[27th Oct., 2022] [ISBI, 2023]
[Paper]
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation
Boah Kim, Yujin Oh, Jong Chul Ye
[19th Sep., 2022] [ICLR, 2023]
[Paper]
Can segmentation models be trained with fully synthetically generated data?
Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges, Petru-Daniel Tudosiu, Mark S Graham, Tom Vercauteren, M Jorge Cardoso
[17th Sep., 2022] [MICCAI Workshop , 2022]
[Paper]
Diffusion Models for Implicit Image Segmentation Ensembles
Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, Philippe C. Cattin
[6th Dec., 2021] [MIDL, 2022]
[Paper] [Github]
Image-to-Image Translation
ContourDiff: Unpaired Image Translation with Contour-Guided Diffusion Models
Yuwen Chen, Nicholas Konz, Hanxue Gu, Haoyu Dong, Yaqian Chen, Lin Li, Jisoo Lee, Maciej A. Mazurowski
[16th Mar., 2024] [arXiv, 2024]
[Paper]
FDDM: Unsupervised Medical Image Translation with a Frequency-Decoupled Diffusion Model
Yunxiang Li, Hua-Chieh Shao, Xiaoxue Qian, You Zhang
[19th Nov., 2023] [arXiv, 2023]
[Paper]
Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study
Jonghun Kim, Hyunjin Park
[1st Nov., 2023] [WACV, 2024]
[Paper] [Github]
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis
Shaoyan Pan, Chih-Wei Chang, Junbo Peng, Jiahan Zhang, Richard L.J. Qiu, Tonghe Wang, Justin Roper, Tian Liu, Hui Mao, Xiaofeng Yang
[28th Apr., 2023] [arXiv, 2023]
[Paper]
Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
Yunxiang Li, Hua-Chieh Shao, Xiao Liang, Liyuan Chen, Ruiqi Li, Steve Jiang, Jing Wang, You Zhang
[5th Apr., 2023] [arXiv, 2023]
[Paper] [Github]
Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels
Jan Oscar Cross-Zamirski, Praveen Anand, Guy Williams, Elizabeth Mouchet, Yinhai Wang, Carola-Bibiane Schönlieb
[15th Mar., 2023] [arXiv, 2023]
[Paper] [Github]
Diffusion Models for Contrast Harmonization of Magnetic Resonance Images
Alicia Durrer, Julia Wolleb, Florentin Bieder, Tim Sinnecker, Matthias Weigel, Robin Sandkühler, Cristina Granziera, Özgür Yaldizli, Philippe C. Cattin
[14th Mar., 2023] [MIDL, 2023]
[Paper]
Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion
Zihao Wang, Yingyu Yang, Maxime Sermesant, Hervé Delingette, Ona Wu
[31st Jan., 2023] [arXiv, 2023]
[Paper]
Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fields
Xie Taofeng, Cao Chentao, Cui Zhuoxu, Li Fanshi, Wei Zidong, Zhu Yanjie, Li Ye, Liang Dong, Jin Qiyu, Chen Guoqing, Wang Haifeng
[16th Nov., 2022] [arXiv, 2022]
[Paper]
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models
Qing Lyu, Ge Wang
[24th Sep., 2022] [arXiv, 2022]
[Paper]
Unsupervised Medical Image Translation with Adversarial Diffusion Models
Muzaffer Özbey, Salman UH Dar, Hasan A Bedel, Onat Dalmaz, Şaban Özturk, Alper Güngör, Tolga Çukur
[17th Jul., 2022] [IEEE TMI Journal, 2022]
[Paper]
A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion
Xiangxi Meng, Yuning Gu, Yongsheng Pan, Nizhuan Wang, Peng Xue, Mengkang Lu, Xuming He, Yiqiang Zhan, Dinggang Shen
[7th Jul., 2022] [arXiv, 2022]
[Paper]
Reconstruction
U2MRPD: Unsupervised undersampled MRI reconstruction by prompting a large latent diffusion model
Ziqi Gao, S. Kevin Zhou
[16th Feb., 2024] [arXiv, 2024]
[Paper]
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]
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]
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]
[Paper] [Github]
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction
Yutong Xie, Quanzheng Li
[5th Mar., 2022] [MICCAI, 2022]
[Paper] [Github]
MRI Reconstruction via Data Driven Markov Chain with Joint Uncertainty Estimation
Guanxiong Luo, Martin Heide, Martin Uecker
[3rd Feb., 2022] [arXiv, 2022]
[Paper] [Github]
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]
[Paper] [Github]
Score-based diffusion models for accelerated MRI
Hyungjin Chung, Jong chul Ye
[8th Oct., 2021] [MIA, 2021]
[Paper] [Github]
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]
[Paper] [Github]
Image Generation
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]
[Paper]
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]
[Paper] [Github]
Generative Enhancement for 3D Medical Images
Lingting Zhu, Noel Codella, Dongdong Chen, Zhenchao Jin, Lu Yuan, Lequan Yu
[14th Mar., 2024] [arXiv, 2024]
[Paper] [Github]
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]
[Paper] [Github]
Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields
Ashish Sinha, Ghassan Hamarneh
[13th Mar., 2024] [arXiv, 2024]
[Paper] [Github]
Federated Data Model
Xiao Chen, Shunan Zhang, Eric Z. Chen, Yikang Liu, Lin Zhao, Terrence Chen, Shanhui Sun
[13th 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] [arXiv, 2024]
[Paper]
An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels
Shumpei Takezaki, Seiichi Uchida
[1st Mar., 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] [arXiv, 2024]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper] [Github]
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] [MICCAI, 2023]
[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]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper] [Github]
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]
[Paper]
Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion
Shaheer U. Saeed, Tom Syer, Wen Yan, Qianye Yang, Mark Emberton, Shonit Punwani, Matthew J. Clarkson, Dean C. Barratt, Yipeng Hu
[3rd Mar., 2023] [arXiv, 2023]
[Paper]
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets
Dennis Eschweiler, Johannes Stegmaier
[2nd Jan., 2023] [arXiv, 2023]
[Paper] [Github] [Synthetic Dataset]
Conversion of the Mayo LDCT Data to Synthetic Equivalent through the Diffusion Model for Training Denoising Networks with a Theoretically Perfect Privacy
Yongyi Shi, Ge Wang
[16th Jan., 2023] [arXiv, 2023]
[Paper]
Generating Realistic 3D Brain MRIs Using a Conditional Diffusion Probabilistic Model
Wei Peng, Ehsan Adeli, Qingyu Zhao, Kilian M Pohl
[15th Dec., 2022] [MICCAI, 2023]
[Paper]
SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation
Jee Seok Yoon, Chenghao Zhang, Heung-Il Suk, Jia Guo, Xiaoxiao Li
[16th Dec., 2022] [arXiv, 2022]
[Paper]
Diffusion Probabilistic Models beat GANs on Medical Images
Gustav Müller-Franzes, Jan Moritz Niehues, Firas Khader, Soroosh Tayebi Arasteh, Christoph Haarburger, Christiane Kuhl, Tianci Wang, Tianyu Han, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
[14th Dec., 2022] [arXiv, 2022]
[Paper]
Improving dermatology classifiers across populations using images generated by large diffusion models
Luke W. Sagers, James A. Diao, Matthew Groh, Pranav Rajpurkar, Adewole S. Adamson, Arjun K. Manrai
[23rd Nov., 2022] [NeurIPS Workshop, 2022]
[Paper]
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding
Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, Juan Helen Zhou
[13th Nov., 2022] [arXiv, 2022]
[Paper]
An unobtrusive quality supervision approach for medical image annotation
Sonja Kunzmann, Mathias Öttl, Prathmesh Madhu, Felix Denzinger, Andreas Maier
[11th Nov., 2022] [arXiv, 2022]
[Paper]
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haarburger, Maximilian Schulze-Hagen, Philipp Schad, Sandy Engelhardt, Bettina Baessler, Sebastian Foersch, Johannes Stegmaier, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
[7th Nov., 2022] [arXiv, 2022]
[Paper] [Github]
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems
Kai Packhäuser, Lukas Folle, Florian Thamm, Andreas Maier
[2nd Nov., 2022] [arXiv, 2022]
[Paper]
Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models
Hazrat Ali, Shafaq Murad, Zubair Shah
[2nd Nov., 2022] [arXiv, 2022]
[Paper]
A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
Puria Azadi Moghadam, Sanne Van Dalen, Karina C. Martin, Jochen Lennerz, Stephen Yip, Hossein Farahani, Ali Bashashati
[27th Sep., 2022] [arXiv, 2022]
[Paper]
Brain Imaging Generation with Latent Diffusion Models
Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
[15th Sep., 2022] [MICCAI Workshop, 2022]
[Paper]
A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images
Dominik J. E. Waibel, Ernst Röell, Bastian Rieck, Raja Giryes, Carsten Marr
[30th Aug., 2022] [arXiv, 2022]
[Paper] [Github]
Diffusion Deformable Model for 4D Temporal Medical Image Generation
Boah Kim, Jong Chul Ye
[27th Jan., 2022] [MICCAI, 2022]
[Paper] [Github]
Three-Dimensional Medical Image Synthesis with Denoising Diffusion Probabilistic Models
Zolnamar Dorjsembe, Sodtavilan Odonchimed, Furen Xiao
[22nd Apr., 2022] [MIDL, 2022]
[Paper] [Github]
Text-to-Image
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning
Woojung Han, Chanyoung Kim, Dayun Ju, Yumin Shim, Seong Jae Hwang
[11th Mar., 2024] [arXiv, 2024]
[Paper]
Synthesizing CTA Image Data for Type-B Aortic Dissection using Stable Diffusion Models
Ayman Abaid, Muhammad Ali Farooq, Niamh Hynes, Peter Corcoran, Ihsan Ullah
[10th Feb., 2024] [arXiv, 2024]
[Paper]
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation
Simeon Allmendinger, Patrick Hemmer, Moritz Queisner, Igor Sauer, Leopold Müller, Johannes Jakubik, Michael Vössing, Niklas Kühl
[5th Dec., 2023] [arXiv, 2023]
[Paper] [Github]
MedXChat: Bridging CXR Modalities with a Unified Multimodal Large Model
Ling Yang, Zhanyu Wang, Luping Zhou
[4th Dec., 2023] [arXiv, 2023]
[Paper]
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
Yu Gu, Jianwei Yang, Naoto Usuyama, Chunyuan Li, Sheng Zhang, Matthew P. Lungren, Jianfeng Gao, Hoifung Poon
[16th Oct., 2023] [arXiv, 2023]
[Paper]
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
Yanwu Xu, Li Sun, Wei Peng, Shyam Visweswaran, Kayhan Batmanghelich
[5th Oct., 2023] [arXiv, 2023]
[Paper] [Github]
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts
Shiyi Du, Xiaosong Wang, Yongyi Lu, Yuyin Zhou, Shaoting Zhang, Alan Yuille, Kang Li, Zongwei Zhou
[4th Oct., 2023] [arXiv, 2023]
[Paper]
PIE: Simulating Disease Progression via Progressive Image Editing
Kaizhao Liang, Xu Cao, Kuei-Da Liao, Tianren Gao, Wenqian Ye, Zhengyu Chen, Jianguo Cao, Tejas Nama, Jimeng Sun
[21st Sep., 2023] [arXiv, 2023]
[Paper]
TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models
Se-In Jang, Cristina Lois, Emma Thibault, J. Alex Becker, Yafei Dong, Marc D. Normandin, Julie C. Price, Keith A. Johnson, Georges El Fakhri, Kuang Gong
[21st Jun., 2023] [arXiv, 2023]
[Paper]
Pay Attention: Accuracy Versus Interpretability Trade-off in Fine-tuned Diffusion Models
Mischa Dombrowski, Hadrien Reynaud, Johanna P. Müller, Matthew Baugh, Bernhard Kainz
[31st Mar., 2023] [arXiv, 2023]
[Paper] [Github]
Medical diffusion on a budget: textual inversion for medical image generation
Bram de Wilde, Anindo Saha, Richard P.G. ten Broek, Henkjan Huisman
[23rd Mar., 2023] [arXiv, 2023]
[Paper]
Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images
Mohamed Akrout, Bálint Gyepesi, Péter Holló, Adrienn Poór, Blága Kincső, Stephen Solis, Katrina Cirone, Jeremy Kawahara, Dekker Slade, Latif Abid, Máté Kovács, István Fazekas
[12th Jan., 2023] [arXiv, 2023]
[Paper]
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier Van der Sluijs, Małgorzata Połacin, Juan Manuel Zambrano Chaves, Tanishq Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari
[23rd Nov., 2022] [arXiv, 2022]
[Paper]
Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
Pierre Chambon, Christian Bluethgen, Curtis P. Langlotz, Akshay Chaudhari
[9th Oct., 2022] [arXiv, 2022]
[Paper]
Registration
FSDiffReg: Feature-wise and Score-wise Diffusion-guided Unsupervised Deformable Image Registration for Cardiac Images
Yi Qin, Xiaomeng Li
[22nd Jul., 2023] [arXiv, 2023]
[Paper] [Github]
DiffuseMorph: Unsupervised Deformable Image Registration Along Continuous Trajectory Using Diffusion Models
Boah Kim, Inhwa Han, Jong Chul Ye
[9th Dec., 2021] [ECCV, 2022]
[Paper]
Classification
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
Xing Shen, Hengguan Huang, Brennan Nichyporuk, Tal Arbel
[10th Nov., 2023] [arXiv, 2023]
[Paper]
Improving Nonalcoholic Fatty Liver Disease Classification Performance With Latent Diffusion Models
Romain Hardy, Cornelia Ilin, Joe Klepich, Ryan Mitchell, Steve Hall, Jericho Villareal
[13th Jul., 2023] [arXiv, 2023]
[Paper]
Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder
Ayodeji Ijishakin, Ahmed Abdulaal, Adamos Hadjivasiliou, Sophie Martin, James Cole
[5th Jun., 2023] [arXiv, 2023]
[Paper]
DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
Yijun Yang, Huazhu Fu, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb, Lei Zhu
[19th Mar., 2023] [arXiv, 2023]
[Paper] [Github]
Object Detection
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Anjany Sekuboyina, Mustafa Gundogar, Bernd Stadlinger, Albert Mehl, Bjoern Menze
[11th Mar., 2023] [arXiv, 2023]
[Paper] [Github]
Image Restoration
Inpainting
Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report
Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani, Mana Moassefi, Sanaz Vahdati, Bradley J. Erickson
[21st Oct., 2022] [arXiv, 2022]
[Paper] [GitHub] [Online Tool]
Super Resolution
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]
Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography
InChan Hwang, MinJae Woo
[8th Oct, 2023] [arXiv, 2023]
[Paper]
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model
Jueqi Wang, Jacob Levman, Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, M. Jorge Cardoso, Razvan Marinescu
[23rd Aug, 2023] [MICCAI, 2023]
[Paper] [GitHub]
Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles
Yuanzheng Ma, Wangting Zhou, Rui Ma, Sihua Yang, Yansong Tang, Xun Guan
[2nd May, 2023] [arXiv, 2023]
[Paper] [GitHub]
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution
Ye Mao, Lan Jiang, Xi Chen, Chao Li
[24th Mar., 2023] [arXiv, 2023]
[Paper]
Enhancement
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
Richard Osuala, Daniel Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia Schnabel, Karim Lekadir
[20th Mar, 2024] [arXiv, 2024]
[Paper] [GitHub]
Step-Calibrated Diffusion for Biomedical Optical Image Restoration
Yiwei Lyu, Sung Jik Cha, Cheng Jiang, Asadur Chowdury, Xinhai Hou, Edward Harake, Akhil Kondepudi, Christian Freudiger, Honglak Lee, Todd C. Hollon
[20th Mar, 2024] [arXiv, 2024]
[Paper] [GitHub]
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
Long Bai, Tong Chen, Yanan Wu, An Wang, Mobarakol Islam, Hongliang Ren
[5th July, 2023] [arXiv, 2023]
[Paper] [GitHub]
Adversarial Attacks
On enhancing the robustness of Vision Transformers: Defensive Diffusion
Raza Imam, Muhammad Huzaifa, Mohammed El-Amine Azz
[14th May, 2023] [arXiv, 2023]
[Paper] [GitHub]
Fight Fire With Fire: Reversing Skin Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism
Yongwei Wang, Yuan Li, Zhiqi Shen
[22nd Aug., 2022] [arXiv, 2022]
[Paper]
Time Series
Time Weaver: A Conditional Time Series Generation Model
Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep Chinchali
[5th Mar., 2024] [arXiv, 2024]
[Paper]
Risk-Sensitive Diffusion: Learning the Underlying Distribution from Noisy Samples
Yangming Li, Max Ruiz Luyten, Mihaela van der Schaar
[3rd Feb., 2024] [arXiv, 2024]
[Paper]
Reliable Generation of EHR Time Series via Diffusion Models
Muhang Tian, Bernie Chen, Allan Guo, Shiyi Jiang, Anru R. Zhang
[23rd Oct., 2023] [arXiv, 2023]
[Paper]
A Comprehensive Survey on Generative Diffusion Models for Structured Data
Heejoon Koo, To Eun Kim
[7th Jun., 2023] [arXiv, 2023]
[Paper]
Restoration of Time-Series Medical Data with Diffusion Model
Jiwoon Lee, Cheolsoo Park
[6th Oct., 2022] [ICCE-Asia, 2022]
[Paper]
Audio
Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance
June-Woo Kim, Chihyeon Yoon, Miika Toikkanen, Sangmin Bae, Ho-Young Jung
[11th Nov., 2023] [NeurIPS Workshop, 2023]
[Paper] [GitHub]
Multi-task
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]
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging
Huidong Xie, Weijie Gan, Bo Zhou, Xiongchao Chen, Qiong Liu, Xueqi Guo, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Chi Liu
[7th Nov., 2023] [arXiv, 2023]
[Paper]
Application-driven Validation of Posteriors in Inverse Problems
Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke, Minu Dietlinde Tizabi, Sebastian Gruber, Dasha Trofimova, Lynton Ardizzone, Paul F. Jaeger, Florian Buettner, Ullrich Köthe, Lena Maier-Hein
[18th Sep., 2023] [arXiv, 2023]
[Paper]
Content-Preserving Diffusion Model for Unsupervised AS-OCT image Despeckling
Li Sanqian, Higashita Risa, Fu Huazhu, Li Heng, Niu Jingxuan, Liu Jiang
[30th June, 2023] [arXiv, 2023]
[Paper]
Anatomically constrained CT image translation for heterogeneous blood vessel segmentation
Giammarco La Barbera, Haithem Boussaid, Francesco Maso, Sabine Sarnacki, Laurence Rouet, Pietro Gori, Isabelle Bloch
[4th Oct., 2022] [BMVC, 2022]
[Paper]
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Walter H. L. Pinaya, Mark S. Graham, Robert Gray, Pedro F Da Costa, Petru-Daniel Tudosiu, Paul Wright, Yee H. Mah, Andrew D. MacKinnon, James T. Teo, Rolf Jager, David Werring, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardos
[7th Jun., 2022] [MICCAI, 2022]
[Paper]
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion
Hyungjin Chung, Eun Sun Lee, Jong Chul Ye
[23rd Mar., 2022] [IEEE TMI, 2022]
[Paper]
Other Applications
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
Zhuang Xiong, Wei Jiang, Yang Gao, Feng Liu, Hongfu Sun
[21st Mar., 2024] [arXiv, 2024] \
[Paper]
DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation
Qilong Zhao, Yifei Zhang, Mengdan Zhu, Siyi Gu, Yuyang Gao, Xiaofeng Yang, Liang Zhao
[16th Feb., 2024] [arXiv, 2024] \
[Paper] [GitHub]
Statistical Test for Generated Hypotheses by Diffusion Models
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi
[19th Feb., 2024] [arXiv, 2024] \
[Paper] [GitHub]
On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI
Daniel McDuff, Tim Korjakow, Scott Cambo, Jesse Josua Benjamin, Jenny Lee, Yacine Jernite, Carlos Muñoz Ferrandis, Aaron Gokaslan, Alek Tarkowski, Joseph Lindley, A. Feder Cooper, Danish Contractor
[7th Feb., 2024] [arXiv, 2024] \
[Paper]
Unconditional Latent Diffusion Models Memorize Patient Imaging Data
Salman Ul Hassan Dar, Marvin Seyfarth, Jannik Kahmann, Isabelle Ayx, Theano Papavassiliu, Stefan O. Schoenberg, Sandy Engelhardt
[1st Feb., 2024] [arXiv, 2024] \
[Paper] [GitHub]
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
[11th Jan., 2024] [arXiv, 2024] \
[Paper] [GitHub]
VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnostics
Ammar A. Siddiqui, Santosh Tirunagari, Tehseen Zia, David Windridge
[2nd Jan., 2024] [arXiv, 2024] \
[Paper]
On the notion of Hallucinations from the lens of Bias and Validity in Synthetic CXR Images
Gauri Bhardwaj, Yuvaraj Govindarajulu, Sundaraparipurnan Narayanan, Pavan Kulkarni, Manojkumar Parmar
[12th Dec., 2023] [arXiv, 2023] \
[Paper]
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining
Tianyu Han, Laura Žigutytė, Luisa Huck, Marc Huppertz, Robert Siepmann, Yossi Gandelsman, Christian Blüthgen, Firas Khader, Christiane Kuhl, Sven Nebelung, Jakob Kather, Daniel Truhn
[29th Sep., 2023] [arXiv, 2023]
[Paper] [GitHub] [Demo]
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context
Rucha Deshpande, Muzaffer Özbey, Hua Li, Mark A. Anastasio, Frank J. Brooks
[19th Sep., 2023] [arXiv, 2023]
[Paper]
Beta quantile regression for robust estimation of uncertainty in the presence of outliers
Haleh Akrami, Omar Zamzam, Anand Joshi, Sergul Aydore, Richard Leahy
[14th Sep., 2023] [arXiv, 2023] \
[Paper]
Unsupervised 3D out-of-distribution detection with latent diffusion models
Mark S. Graham, Walter Hugo Lopez Pinaya, Paul Wright, Petru-Daniel Tudosiu, Yee H. Mah, James T. Teo, H. Rolf Jäger, David Werring, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
[7th Jul., 2023] [MICCAI, 2023] \
[Paper] [GitHub]
DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy
Yiwen Zhang, Chuanpu Li, Liming Zhong, Zeli Chen, Wei Yang, Xuetao Wang
[28th Jun., 2023] [arXiv, 2023]
[Paper]
Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading
Matthias Keicher, Matan Atad, David Schinz, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Nassir Navab
[21st Mar., 2023] [arXiv, 2023]
[Paper]
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image
Zhuchen Shao, Liuxi Dai, Yifeng Wang, Haoqian Wang, Yongbing Zhang
[11th Mar., 2023] [arXiv, 2023]
[Paper]
Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline
Xuhang Chen, Baiying Lei, Chi-Man Pun, Shuqiang Wang
[11th Mar., 2023] [arXiv, 2023]
[Paper]
Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement
Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang
[8th Mar., 2023] [arXiv, 2023]
[Paper]
DiffusionCT: Latent Diffusion Model for CT Image Standardization
Md Selim, Jie Zhang, Michael A. Brooks, Ge Wang, Jin Chen
[20th Jan., 2023] [arXiv, 2023]
[Paper]
Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification
Shizhan Gong, Cheng Chen, Yuqi Gong, Nga Yan Chan, Wenao Ma, Calvin Hoi-Kwan Mak, Jill Abrigo, Qi Dou
[1st Jan., 2023] [arXiv, 2023]
[Paper]
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