Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original and synthetic datasets. If diffusion-generated samples can help CNN models perform comparably to those trained on original datasets, reliance on patient-specific data for training CNNs might be reduced. In this study, we investigated the effectiveness of diffusion models for generating synthetic medical images to train CNNs in three domains: Brain Tumor MRI, Acute Lymphoblastic Leukemia (ALL), and SARS-CoV-2 CT scans. A diffusion model was trained to generate synthetic datasets for each domain. Pre-trained CNN architectures were then trained on these synthetic datasets and evaluated on unseen real data. All three datasets achieved promising classification performance using CNNs trained on synthetic data. Local Interpretable Model-Agnostic Explanations (LIME) analysis revealed that the models focused on relevant image features for classification. This study demonstrates the potential of diffusion models to generate synthetic medical images for training CNNs in medical image analysis.
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Iran University of Science and Technology（伊朗科技大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；RWTH Aachen University（亚琛工业大学）；Microsoft（微软）；University of British Columbia（不列颠哥伦比亚大学）；University of Regensburg（雷根斯堡大学）
In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. We combine the deterministic iterative noising and denoising scheme with classifier guidance for image-to-image translation between d…
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis
Heidelberg University Hospital（海德堡大学医院）；German Centre for Cardiovascular Research（德国心血管研究中心）；Heidelberg University（海德堡大学）；University Medical Center Mannheim（曼海姆大学医学中心）
MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZIU-UIUC Institute, Zhejiang University（浙江大学ZIU-UIUC研究院）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）
Harbin Institute of Technology at Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）
Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance. However, DDPM requires many iterative denoising steps to generate segmentations from Gaussian noise, resulting in extremely inefficient inference. To mitigate the issue, we propose a principled acceleration strategy, called pre-segmentation diffusion sampling DDPM (PD-DDPM), which is specially …
Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation
Yale University（耶鲁大学）；University of California Irvine（加州大学欧文分校）；University of Science and Technology of China（中国科学技术大学）；Case Western Reserve University（凯斯西储大学）；Northwestern University（西北大学）；Yale School of Medicine（耶鲁医学院）
Denoising Diffusion Models for Anomaly Localization in Medical Images
School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany（生物医学影像机器学习研究所，海德堡慕尼黑德国）；Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（巴塞尔大学生物医学工程系，瑞士Allschwil）；School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom（伦敦国王学院生物医学工程与影像科学学院，英国）；Department of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA（耶鲁大学医学院生物医学信息学与数据科学系，美国新罕布什尔州新 Haven）；Yale Biomedical Imaging Institute, Yale University, New Haven, CT, USA（耶鲁大学生物医学影像研究院，美国新罕布什尔州新 Haven）
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）；Manchester University NHS Foundation Trust（曼彻斯特大学国民保健服务基金会信托）
Generative models are becoming popular for the synthesis of medical images. Recently, neural diffusion models have demonstrated the potential to generate photo-realistic images of objects. However, their potential to generate medical images is not explored yet. In this work, we explore the possibilities of synthesis of medical images using neural diffusion models. First, we use a pre-trained DALLE2 model to generate lungs X-Ray and CT images from an input text prompt. Second, we train a stable diffusion model with 3165 X-Ray images and generate synthetic images. We evaluate the synthetic image…
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
Technical University of Munich（慕尼黑工业大学）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Universitat de Barcelona（巴塞罗那大学）；Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；Helmholtz AI（亥姆霍兹人工智能研究所）
本文提出基于医学属性条件的 Latent Diffusion Models，通过差分隐私训练生成 3D 心脏 MRI 图像，并首次在 3D 医学图像生成中量化差分隐私，发现预训练显著提升图像质量。
Cross-conditioned Diffusion Model for Medical Image to Image Translation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi’an Jiaotong University（西安交通大学）；The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology（香港科技大学）
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural images have been extensively studied, approaches for temporal medical image generation such as 4D cardiac volume data are limited. In this work, we present a novel deep learning model that generates intermediate temporal volumes between source and target volumes. Specifically, we propose a diffusion deformable model (DDM) by adapting the denoising diffusion probabilistic model th…
Temporal Evolution of Knee Osteoarthritis: A Diffusion-based Morphing Model for X-ray Medical Image Synthesis
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Orleans（奥尔良大学）；IDP Institute（IDP研究所）；Jiangsu Institute of Hematology（江苏省血液研究所）；First Affiliated Hospital of Soochow University（苏州大学附属第一医院）；Cancer Hospital of Pernambuco（伯南布哥癌症医院）
VolDiT: Controllable Volumetric Medical Image Synthesis with Diffusion Transformers
Institute for Artificial Intelligence in Cardiovascular Medicine, Medical Faculty of Heidelberg University, Heidelberg University（海德堡大学医学院心血管医学人工智能研究所）；Department of Cardiology, Angiology, Pneumology, Heidelberg University Hospital（海德堡大学医院心脏病学、血管学、肺病学系）；DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim（德国心血管研究中心（DZHK）海德堡/曼海姆合作站点）；University Medical Center of the Johannes Gutenberg-University Mainz（美因茨约翰内斯·古腾堡大学医学中心）
MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Peking University Shenzhen Graduate School South China University of Technology, China
PPDM: Pixel Puzzling Diffusion Model for Speed and Memory Efficient Volumetric Medical Image Translation
Department of Radiology, Northwestern University（西北大学放射学系）；Department of Biomedical Engineering, Yale University（耶鲁大学生物医学工程系）；Department of Radiology and Biomedical Imaging, Yale School of Medicine（耶鲁医学院放射学与生物医学影像系）
Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
National University of Singapore（国立新加坡大学）；National University of Singapore Suzhou Research Institute（国立新加坡大学苏州研究院）；Wenzhou-Kean University（温州-凯恩大学）；Soochow University（苏州大学）；City University of Hong Kong（香港城市大学）
Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions
Aeronautics Institute of Technology（航空技术学院）；Science and Technology Institute, Federal University of São Paulo（圣保罗联邦大学科学与技术研究所）；LASIGE, Faculdade de Ciências, Universidade de Lisboa（里斯本大学理学院LASIGE实验室）；University of Coimbra, CISUC/LASI – Centre for Informatics and Systems of the University of Coimbra（科英布拉大学CISUC/LASI - 科英布拉大学信息与系统中心）；Instituto Superior Técnico, Universidade de Lisboa, Instituto de Telecomunicações（里斯本大学高等技术学院、电信研究所）
FlexiD-Fuse: Flexible number of inputs multi-modal medical image fusion based on diffusion model
School of Physics（物理学院）；Optoelectronic Engineering, Foshan University（光电工程学院，佛山大学）；Guangdong-HongKong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology（粤港澳联合智能微纳光电技术实验室）；Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology（广东省工业智能检测技术重点实验室）；School of Information Engineering（信息工程学院）；Automation, Kunming University of Science（自动化系，昆明理工大学）
Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the computer vision community which has gained significant attention from the last few years in identifying the internal structure of multimodal medical imaging data. The adversarial network simultaneously generates realistic medical images and corresponding annotations, which proven to be useful in many cases such as image augmentation, image registration, medical image generation, image reconstruction, and image-to-image translation. These properties bring the attention of the researcher in the field of medical…
Zhejiang University-University of Illinois Urbana-Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合学院）；Zhejiang University（浙江大学）
Reducing the radiation dose in computed tomography (CT) is important to mitigate radiation-induced risks. One option is to employ a well-trained model to compensate for incomplete information and map sparse-view measurements to the CT reconstruction. However, reconstruction from sparsely sampled measurements is insufficient to uniquely characterize an object in CT, and a learned prior model may be inadequate for unencountered cases. Medical modal translation from magnetic resonance imaging (MRI) to CT is an alternative but may introduce incorrect information into the synthesized CT images in a…
Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Models
AIRI；HSE University（俄罗斯高等经济大学）；Simula Metropolitan Center for Digital Engineering（Simula 数字工程研究中心）；Oslo Metropolitan University（奥斯陆 Metropolitan 大学）；Kazan Federal University（卡扎ン联邦大学）
