Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI
Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain（巴塞罗那大学人工智能医学实验室（BCN-AIM）、数学与计算机科学系，西班牙）；Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain（加泰罗尼亚高级研究机构（ICREA），巴塞罗那，西班牙）
Joan Perramon-Llussà, Amelia Jiménez-Sánchez, Grzegorz Skorupko, Fotis Avgoustidis, Carlos Martín-Isla, Karim Lekadir, Polyxeni Gkontra
基础模型（FMs）在医疗影像任务和模态中表现出强大的鲁棒性，尤其在心脏磁共振（CMR）中，通过任务特定的适应。然而，使用单个站点数据进行适应可能导致性能不佳和模型偏见，而集中化微调临床数据在隐私约束下往往不可行。联邦微调提供了一种隐私保护的替代方案；然而，传统方法在异构、非独立同分布（non-IID）多中心数据下表现不佳，并且在适应大模型时通信开销巨大。在本文中，我们研究了联邦FM微调用于3D CMR疾病检测，并提出Med-DualLoRA，一种客户端感知的参数高效微调（PEFT）联邦框架，通过加法分解解耦全局共享和局部低秩适应（LoRA）。全局和局部LoRA模块在本地训练，但只有全局组件在各站点之间共享和聚合，保持本地适配器的隐私。这种设计提高了个性化能力，同时显著降低了通信成本，实验表明仅适应两个transformer块可以保持性能并进一步提高效率。我们评估了我们的方法在多中心最先进的 cine 3D CMR FM 上，使用ACDC和结合M\&Ms数据集进行疾病检测的联邦学习，将每个供应商视为联邦客户端。Med-DualLoRA在平衡准确率（0.768）和特异性（0.612）上优于其他联邦PEFT基线，同时保持通信效率。我们的方法在现实临床约束下为医学FM的本地联邦适应提供了可扩展的解决方案。
英文摘要：
Foundation models (FMs) show great promise for robust downstream performance across medical imaging tasks and modalities, including cardiac magnetic resonance (CMR), following task-specific adaptation. However, adaptation using single-site data may lead to suboptimal performance and increased model bias, while centralized fine-tuning on clinical data is often infeasible due to privacy constraints. Federated fine-tuning offers a privacy-preserving alternative; yet conventional approaches struggle under heterogeneous, non-IID multi-center data and incur substantial communication overhead when adapting large models. In this work, we study federated FM fine-tuning for 3D CMR disease detection and propose Med-DualLoRA, a client-aware parameter-efficient fine-tuning (PEFT) federated framework that disentangles globally shared and local low-rank adaptations (LoRA) through additive decomposition. Global and local LoRA modules are trained locally, but only the global component is shared and aggregated across sites, keeping local adapters private. This design improves personalization while significantly reducing communication cost, and experiments show that adapting only two transformer blocks preserves performance while further improving efficiency. We evaluate our method on a multi-center state-of-the-art cine 3D CMR FM fine-tuned for disease detection using ACDC and combined M\&Ms datasets, treating each vendor as a federated client. Med-DualLoRA achieves statistically significant improved performance (balanced accuracy 0.768, specificity 0.612) compared to other federated PEFT baselines, while maintaining communication efficiency. Our approach provides a scalable solution for local federated adaptation of medical FMs under realistic clinical constraints.
We propose to learn a probabilistic motion model from a sequence of images. Besides spatio-temporal registration, our method offers to predict motion from a limited number of frames, useful for temporal super-resolution. The model is based on a probabilistic latent space and a novel temporal dropout training scheme. This enables simulation and interpolation of realistic motion patterns given only one or any subset of frames of a sequence. The encoded motion also allows to be transported from one subject to another without the need of inter-subject registration. An unsupervised generative defor…
University of Edinburgh（爱丁堡大学）；Edinburgh Imaging Facility QMRI（爱丁堡影像设施QMRI）；Centre for Cardiovascular Science（心血管科学中心）；Cedars Sinai Medical Center（西达赛奈医疗中心）；The Alan Turing Institute（艾伦·图灵研究所）
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in this way. We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics. Here, we explicitly learn this decomposed (disentangled) representation of imaging data, focusing in particular on cardiac images. We propose Spatial Decomposition Netw…
Imperial College London（帝国理工学院）；University of Edinburgh（爱丁堡大学）；Technical University of Munich（慕尼黑工业大学）
Artificial intelligence (AI) and Machine Learning (ML) have shown great potential in improving the medical imaging workflow, from image acquisition and reconstruction to disease diagnosis and treatment. Particularly, in recent years, there has been a significant growth in the use of AI and ML algorithms, especially Deep Learning (DL) based methods, for medical image reconstruction. DL techniques have shown to be competitive and often superior over conventional reconstruction methods in terms of both reconstruction quality and computational efficiency. The use of DL-based image reconstruction a…
Imperial College London（帝国理工学院）；MRC London Clinical Sciences Centre（MRC伦敦临床科学中心）；National Heart & Lung Institute（国家心肺研究所）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学研究生医学院）
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical…
Personalized 4D Whole Heart Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院）；Department of Engineering Science, University of Oxford, Oxford, UK（牛津大学工程科学系）；Department of Medicine, National University of Singapore, Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore, Singapore（新加坡国立心脏中心心内科部）
CONICET - FCEN, Universidad Nacional de Cuyo（阿根廷国家科学与技术研究理事会 - 库约国立大学自然科学学院）；Centro Integral de Medicina Nuclear y Radioterapia Bariloche（巴里洛切核医学与放射治疗综合中心）
Cardiac function is of paramount importance for both prognosis and treatment of different pathologies such as mitral regurgitation, ischemia, dyssynchrony and myocarditis. Cardiac behavior is determined by structural and functional features. In both cases, the analysis of medical imaging studies requires to detect and segment the myocardium. Nowadays, magnetic resonance imaging (MRI) is one of the most relevant and accurate non-invasive diagnostic tools for cardiac structure and function. In this work we propose to use a deep learning technique to assist the automatization of myocardial segmen…
Localization of anatomical structures is a prerequisite for many tasks in medical image analysis. We propose a method for automatic localization of one or more anatomical structures in 3D medical images through detection of their presence in 2D image slices using a convolutional neural network (ConvNet). A single ConvNet is trained to detect presence of the anatomical structure of interest in axial, coronal, and sagittal slices extracted from a 3D image. To allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling is applied. After detection, 3D bounding boxes are created…
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Medical image analysis, especially segmenting a specific organ, has an important role in developing clinical decision support systems. In cardiac magnetic resonance (MR) imaging, segmenting the left and right ventricles helps physicians diagnose different heart abnormalities. There are challenges for this task, including the intensity and shape similarity between left ventricle and other organs, inaccurate boundaries and presence of noise in most of the images. In this paper we propose an automated method for segmenting the left ventricle in cardiac MR images. We first automatically extract th…
In most medical image processing tasks, the orientation of an image would affect computing result. However, manually reorienting images wastes time and effort. In this paper, we study the problem of recognizing orientation in cardiac MRI and using deep neural network to solve this problem. For multiple sequences and modalities of MRI, we propose a transfer learning strategy, which adapts our proposed model from a single modality to multiple modalities. We also propose a prediction method that uses voting. The results shows that deep neural network is an effective way in recognition of cardiac …
University of Sherbrooke（舍布鲁克大学）；University of Bourgogne Franche-Comte（勃艮第-弗朗什孔泰大学）；CASIS inc.（CASIS公司）；University of Lyon（里昂大学）
Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs are not immune to producing anatomically inaccurate segmentations, even when built upon a shape prior. In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability. The idea behind our method is to use a well-trai…
Beijing University of Posts and Telecommunications（北京邮电大学）
Medical image segmentation is one of the important tasks of computer-aided diagnosis in medical image analysis. Since most medical images have the characteristics of blurred boundaries and uneven intensity distribution, through existing segmentation methods, the discontinuity within the target area and the discontinuity of the target boundary are likely to lead to rough or even erroneous boundary delineation. In this paper, we propose a new iterative refined interactive segmentation method for medical images based on agent reinforcement learning, which focuses on the problem of target segmenta…
Rutgers University（罗格斯大学）；New York University School of Medicine（纽约大学医学院）；NVIDIA（英伟达）；SenseBrain；Shanghai AI Laboratory（上海人工智能实验室）
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation. However, this technique has not been widely used in clinical diagnosis, as a result of the difficulty of motion tracking encountered with t-MRI images. In this paper, we propose a novel deep learning-based fully unsupervised method for in vivo motion tracking on t-MRI images. We first estimate the motion field (INF) between any two consecutive t-MRI frames by a bi-directional generative diffeomorphic registration neural network. Using this result, we then e…
Harbin Institute of Technology（哈尔滨工业大学）；University of Manchester（曼彻斯特大学）
Left ventricular (LV) volumes estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address direct LV volumes prediction task. Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN). We study the end-to-end LV volumes prediction method in items of the data preprocessing, networks structure, and multi-views fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR)…
Fraunhofer Institute for Telecommunications–Heinrich Hertz Institute（弗劳恩霍夫电信研究所-海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Experimental and Clinical Research Center（实验与临床研究中心）；Charité Medical Faculty（夏里特医学院）；Max-Delbrueck Center for Molecular Medicine（马克斯·德尔布吕克分子医学中心）；HELIOS Klinikum Berlin Buch（柏林布赫赫利俄斯医院）；Physikalisch-Technische Bundesanstalt (PTB)（德国联邦物理技术研究院（PTB））；King’s College London（伦敦国王学院）
High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times. Here we present a novel compressed sensing (CS) reconstruction approach using shearlets as a sparsifying transform allowing for fast 3D CMR (3DShearCS). Shearlets are mathematically optimal for a simplified model of natural images and have been proven to be more efficient than classical systems such as wavelets. Data is acquired with a 3D Radial Phase Encoding (RPE) trajectory and an it…
Many strides have been made in semantic segmentation of multiple classes within an image. This has been largely due to advancements in deep learning and convolutional neural networks (CNNs). Features within a CNN are automatically learned during training, which allows for the abstraction of semantic information within the images. These deep learning models are powerful enough to handle the segmentation of multiple classes without the need for multiple networks. Despite these advancements, few attempts have been made to automatically segment multiple anatomical features within medical imaging d…
University of Bourgogne Franche-Comte（勃艮第-弗朗什-孔泰大学）；CASIS inc.（CASIS公司）；University of Sherbrooke（舍布鲁克大学）；University Hospital of Dijon（第戎大学医院）
Deep learning methods are the de-facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application which, like many others, requires a large number of annotated data so a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated datasets that machine learning can successfully train on. To do so, we gauged the performance o…
Rutgers University（罗格斯大学）；United Imaging Intelligence（联影智能）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Accurate estimation of shape thickness from medical images is crucial in clinical applications. For example, the thickness of myocardium is one of the key to cardiac disease diagnosis. While mathematical models are available to obtain accurate dense thickness estimation, they suffer from heavy computational overhead due to iterative solvers. To this end, we propose novel methods for dense thickness estimation, including a fast solver that estimates thickness from binary annular shapes and an end-to-end network that estimates thickness directly from raw cardiac images.We test the proposed model…
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；Helmholtz AI, Helmholtz Munich（亥姆霍兹慕尼黑人工智能中心）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Biomedical Engineering and Imaging Sciences, King's College London（伦敦国王学院生物医学工程与影像科学学院）
King's College London（伦敦国王学院）；Guy’s and St Thomas’ Hospital（盖伊和圣托马斯医院）
In computer vision there has been significant research interest in assessing potential demographic bias in deep learning models. One of the main causes of such bias is imbalance in the training data. In medical imaging, where the potential impact of bias is arguably much greater, there has been less interest. In medical imaging pipelines, segmentation of structures of interest plays an important role in estimating clinical biomarkers that are subsequently used to inform patient management. Convolutional neural networks (CNNs) are starting to be used to automate this process. We present the fir…
The goal of this work is to identify the best optimizers for deep learning in the context of cardiac image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly optimizing neural networks within the accelerated schemes category. By revealing the potential interplay between these two types of algorithms (LR and momentum optimizers or momentum rate (MR) in sho…
Universidade Federal Fluminense (UFF)（弗鲁米嫩塞联邦大学）；Universidade Federal do Rio de Janeiro (UFRJ)（里约热内卢联邦大学）；Universidade Federal do Espírito Santo (UFES)（圣埃斯皮里图联邦大学）
The deposits of fat on the surroundings of the heart are correlated to several health risk factors such as atherosclerosis, carotid stiffness, coronary artery calcification, atrial fibrillation and many others. These deposits vary unrelated to obesity, which reinforces its direct segmentation for further quantification. However, manual segmentation of these fats has not been widely deployed in clinical practice due to the required human workload and consequential high cost of physicians and technicians. In this work, we propose a unified method for an autonomous segmentation and quantification…
In the field of medical image, deep convolutional neural networks(ConvNets) have achieved great success in the classification, segmentation, and registration tasks thanks to their unparalleled capacity to learn image features. However, these tasks often require large amounts of manually annotated data and are labor-intensive. Therefore, it is of significant importance for us to study unsupervised semantic feature learning tasks. In our work, we propose to learn features in medical images by training ConvNets to recognize the geometric transformation applied to images and present a simple self-…
ScarNet: A Novel Foundation Model for Automated Myocardial Scar Quantification from LGE in Cardiac MRI
Northwestern University（西北大学）；Feinberg School of Medicine（费林伯格医学院）；University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；Cedars-Sinai Medical Center（西达赛奈医疗中心）
In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model is deployed into the real clinical world, the model may not perform optimally. A major challenge is the potential poor-quality segmentations generated due to degraded image quality or domain shift issues. There is a timely need to develop an automated quality control method that can detect poor segmentations and feedback to clinicians. Here we propose a novel deep generative model-based framework for quality control …
Shape of my heart: Cardiac models through learned signed distance functions
Institute for Applied Mathematics, University of Bonn（波恩大学应用数学研究所）；University of Graz（格拉茨大学）；Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile（智利天主教大学生物与医学工程研究所）；Center for Computational Medicine in Cardiology, Università della Svizzera italiana（瑞士意大利语区大学心血管计算医学中心）；FernUni Schweiz（瑞士远程教育大学）；Instituto Cardiocentro Ticino, EOC（提契诺心脏中心研究所（EOC））；University of Trento（特伦托大学）
Interpretable Representation Learning of Cardiac MRI via Attribute Regularization
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；Helmholtz AI, Helmholtz Munich（亥姆霍兹慕尼黑亥姆霍兹人工智能研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Training deep learning models on cardiac magnetic resonance imaging (CMR) can be a challenge due to the small amount of expert generated labels and inherent complexity of data source. Self-supervised contrastive learning (SSCL) has recently been shown to boost performance in several medical imaging tasks. However, it is unclear how much the pre-trained representation reflects the primary organ of interest compared to spurious surrounding tissue. In this work, we evaluate the optimal method of incorporating prior knowledge of anatomy into a SSCL training paradigm. Specifically, we evaluate usin…
National Heart & Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep l…
Cardiac-CLIP: A Vision-Language Foundation Model for 3D Cardiac CT Images
Southeast University（东南大学）；Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University（南京大学医学院附属金陵医院）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Shanghai AI Laboratory（上海人工智能实验室）；Nanjing First Hospital, Nanjing Medical University（南京医科大学附属南京医院）；Northern Jiangsu People’s Hospital（苏北人民医院）；the Affiliated Hospital of Jiangnan University（江南大学附属医院）；Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院附属邵逸夫医院）；Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院）；Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属第一人民医院）；Guangdong Provincial People’s Hospital（广东省人民医院）；Xijing Hospital, Air Force Medical University（空军军医大学西京医院）；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院）；Sir Run Run Hospital, Nanjing Medical University（南京医科大学附属逸夫医院）；Taizhou People’s Hospital（泰州市人民医院）；Case Western Reserve University（凯斯西储大学）
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice. In this work we introduce a convolutional neural network model for identifying disease in temporal sequences of cardiac MR segmentations which is interpretable in terms of clinically familiar measurements. The model is based around a variational autoencoder, reducing the input into a low-dimensional latent space in which classification occurs. We then use the recently developed `conce…
Single-photon emission computed tomography (SPECT) is a widely applied imaging approach for diagnosis of coronary artery diseases. Attenuation maps (u-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT and CT are obtained sequentially in clinical practice, which potentially induces misregistration between the two scans. Convolutional neural networks (CNN) are powerful tools for medical image registration. Previous CNN-based methods for cross-modality registration either directly concatenated t…
King's College London（伦敦国王学院）；Imperial College London（帝国理工学院）；Guy’s and St Thomas' Hospital（盖伊和圣托马斯医院）
Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these models to explain their decisions is important for fostering clinical trust and facilitating clinical translation. Furthermore, for many problems in medicine there is a wealth of existing clinical knowledge to draw upon, which may be useful in generating explanations, but it is not obvious how this knowledge can be encoded into DL models - most models are learnt either from scratch or using transfer learning from a dif…
Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification
Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine（放射科与放射科学系，约翰霍普金斯大学医学院）；Department of Electrical and Computer Engineering, Johns Hopkins University（电气与计算机工程系，约翰霍普金斯大学）；Department of Computer Science, University of North Carolina at Chapel Hill（计算机科学系，北卡罗来纳大学教堂山分校）；Department of Radiology, University of Colorado Denver Anschutz Medical Campus（放射科，科罗拉多大学丹佛分校安舒茨医学中心）；Department of Radiology, Second Xiangya Hospital, Central South University（放射科，中南大学湘雅医院）；Department of Cardiology, Johns Hopkins University School of Medicine（心血管科，约翰霍普金斯大学医学院）；Department of Diagnostic Imaging, Brown University Health（诊断影像科，布朗大学健康中心）
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Imperial College London（帝国理工学院）；Universitat de Barcelona（巴塞罗那大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Harbin Institute of Technology（哈尔滨工业大学）；Southern University of Science and Technology（南方科技大学）；Technical University of Munich（慕尼黑工业大学）；INRIA（法国国家信息与自动化研究所）；Université Côte d’Azur（蔚蓝海岸大学）；NVIDIA（英伟达）；Xiamen University（厦门大学）；Sichuan University（四川大学）；Tencent AI Lab（腾讯人工智能实验室）
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, automated segmentation of LGE CMR is still challenging, due to the indistinguishable boundaries, heterogeneous intensity distribution and complex enhancement patterns of pathological myocardium from LGE CMR. Furthermore, compared with the other sequences LG…
From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
Gran Sasso Science Institute (GSSI)（大萨索科学研究所）；European Hospital（欧洲医院）；University of Edinburgh（爱丁堡大学）；Physics of Fluids Group, University of Twente（特温特大学流体物理小组）；INFN–Laboratori Nazionali del Gran Sasso（意大利国家核物理研究所大萨索国家实验室）
SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science, Peking University（北京大学计算机学院）
Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）；Department of Computing, Imperial College London（帝国理工学院计算机系）；Department of Electrical Engineering and Computer Science, University of Michigan（密歇根大学电气工程与计算机科学系）；Imsight Medical Technology, Co, Ltd., China（中国医视医疗科技有限公司）；School of Data Science, Fudan University, China（复旦大学数据科学学院）
Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning t…
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；Ultromics Ltd.（奥特罗米克斯有限公司）；University of Oxford（牛津大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Fraunhofer MEVIS（弗劳恩霍夫医学图像计算研究所）；Radboud UMC（拉德堡德大学医学中心）；University of Lübeck（吕贝克大学）
While deep learning has achieved significant advances in accuracy for medical image segmentation, its benefits for deformable image registration have so far remained limited to reduced computation times. Previous work has either focused on replacing the iterative optimization of distance and smoothness terms with CNN-layers or using supervised approaches driven by labels. Our method is the first to combine the complementary strengths of global semantic information (represented by segmentation labels) and local distance metrics that help align surrounding structures. We demonstrate significant …
Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate this shortcoming and identify the under-performing classes, we propose maintaining a confidence array that records class-wise performance during training. A fuzzy fusion of these confidence scores is proposed to adaptively prioritize individual confidence metrics in every sample rat…
Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation
Yale University（耶鲁大学）；University of Pennsylvania（宾夕法尼亚大学）；Washington University School of Medicine in St Louis（华盛顿大学圣路易斯医学学院）；University of South Carolina School of Medicine（南卡罗来纳大学医学院）；University of Colorado Boulder（科罗拉多大学波德分校）
Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRI
School of Computation, Information and Technology（计算信息学院）；Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；School of Medicine, Klinikum Rechts der Isar（医学院，莱茵河右岸医院）；Department of Quantitative Biomedicine（定量生物医学系）；University of Zurich（苏黎世大学）；Department of Computing（计算系）；Imperial College London（伦敦帝国理工学院）
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）
Current state-of-the-art deep learning segmentation methods have not yet made a broad entrance into the clinical setting in spite of high demand for such automatic methods. One important reason is the lack of reliability caused by models that fail unnoticed and often locally produce anatomically implausible results that medical experts would not make. This paper presents an automatic image segmentation method based on (Bayesian) dilated convolutional networks (DCNN) that generate segmentation masks and spatial uncertainty maps for the input image at hand. The method was trained and evaluated u…
