Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. In order to overcome this challenge, we propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc. With the motivation of circumventing this challenge, we propose a cross-site modeling for MR image reconstruction in which the learned intermediate latent features among different source sites are aligned with the distribution of the latent features at the target site. Extensive experiments are conducted to provide various insights about FL for MR image reconstruction. Experimental results demonstrate that the proposed framework is a promising direction to utilize multi-institutional data without compromising patients' privacy for achieving improved MR image reconstruction. Our code will be available at https://github.com/guopengf/FLMRCM.
Johns Hopkins University（约翰斯·霍普金斯大学）；NVIDIA（英伟达）；University of Pittsburgh（匹兹堡大学）；National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；ASST Santi Paolo e Carlo（ASST圣保禄和圣卡洛医院）；University of Milan（米兰大学）
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions, standard FL methods are unstable and require intensive hyperparameter tuning to achieve optimal performance. Conventional hyperparameter optimization algorithms are impractical in real-world FL applications as they involve numerous training trials, which are often not affordabl…
National Taiwan University（台湾大学）；Nagoya University（名古屋大学）；NVIDIA Corporation（英伟达公司）；Aichi Cancer Center（爱知癌症中心）；National Taiwan University Hospital（台湾大学医学院附设医院）
The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models…
Johns Hopkins University（约翰斯·霍普金斯大学）；NVIDIA（英伟达）；Japan Self-Defense Forces Central Hospital（日本自卫队中央医院）；Xiangyang NO.1 People Hospital（襄阳市第一人民医院）；National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；ASST Santi Paolo e Carlo（圣保罗与卡洛医院）；University of Milan（米兰大学）；Foundation IRCCS Ospedale Maggiore Policlinico Hospital（IRCCS马焦雷综合医院基金会）
Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standard algorithm that uses fixed weights, often originating from the dataset sizes at each client, to aggregate the distributed learned models on a server during the FL process. However, non-identical data distribution across clients, known as the non-i.i.d problem in FL, could make this assumption for setting fixed aggregation weights sub-optimal. In this work, we design a new data-driven approach, namely Auto-FedAvg, wh…
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
Yale University（耶鲁大学）；University of Southern California（南加州大学）；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）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）；University of Bern（伯尔尼大学）；Technical University of Munich（慕尼黑工业大学）
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging
College of Computer Science, Sichuan University（四川大学计算机学院）；School of Cyber Science and Engineering, Sichuan University（四川大学网络空间安全学院）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）
MammoFL: Mammographic Breast Density Estimation using Federated Learning
University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）；GSK（葛兰素史克）；Indiana University School of Medicine（印第安纳大学医学院）；Mayo Clinic（梅奥诊所）
Bilkent University（比尔肯特大学）；University of Southern California（南加州大学）
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has recently been introduced to address privacy concerns by enabling distributed training without transfer of imaging data. Existing FL methods for MRI reconstruction employ conditional models to map from undersampled to fully-sampled acquisitions via explicit knowledge of the imaging operator. Since conditional models generalize poorly across different acceleration rates or sampling densities, imaging operators mus…
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
Institute of Biomedical Engineering（生物医学工程研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工大学）；Department of Radiation Oncology（放射肿瘤科）；University Medical Center Schleswig-Holstein（什勒斯威-霍斯特大学医院）；Department of Experimental and Clinical Medicine（实验与临床医学系）；Magna Graecia University（马格拉西亚大学）；Department of Information Engineering（信息工程系）；Università Politecnica delle Marche（帕尔米拉理工大学）
TU Darmstadt（达姆施塔特工业大学）；University Hospital Cologne（科隆大学医院）
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets that reflect the data diversity of many laboratories. Further, the effort of dataset construction can be divided among many. Unfortunately, existing algorithms cannot be easily applied to computational pathology since previous work presupposes that data distributions of laboratories must be similar. This is an unlikely assumption, mainly since dif…
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data
Sano Centre for Computational Medicine（Sano计算医学中心）；AGH University of Krakow（克拉科夫AGH科技大学）；LPI, ETSI Telecomunicación, Universidad de Valladolid（巴利亚多利德大学电信工程学院LPI）
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration
Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；National Taiwan University（台湾大学）；Taipei Veterans General Hospital（台北荣民总医院）；National Yang Ming Chiao Tung University（阳明交通大学）
From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland Institute for Health Computing（马里兰大学健康计算研究所）；St. Jude Children’s Research Hospital（圣犹大儿童研究医院）
University of Maryland School of Medicine（马里兰大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease. In real-world clinical use, multiple diseases need to be considered since they can co-exist in the same patient. In this work, we demonstrate how federated learning (FL) can be used to make these toy CXR datasets from Kaggle clinically useful. Specifically, we train a single FL classification model (`global`) using two separate CXR datasets -- one annotated for presence of pneumonia and the …
Bilkent University（比尔肯特大学）；University of Southern California（南加州大学）
Multi-institutional collaborations are key for learning generalizable MRI synthesis models that translate source- onto target-contrast images. To facilitate collaboration, federated learning (FL) adopts decentralized training and mitigates privacy concerns by avoiding sharing of imaging data. However, FL-trained synthesis models can be impaired by the inherent heterogeneity in the data distribution, with domain shifts evident when common or variable translation tasks are prescribed across sites. Here we introduce the first personalized FL method for MRI Synthesis (pFLSynth) to improve reliabil…
FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation
CitAI Research Centre, Department of Computer Science City St. George's, University of London（CitAI研究中心、计算机科学系（City St. George's大学））；BioMedIA, Department of Computing, Imperial College London（BioMedIA、计算系（帝国理工学院伦敦））
Technical University of Munich（慕尼黑工业大学）；OpenMined（OpenMined（开放隐私计算社区））；Imperial College London（帝国理工学院）
Collaborative machine learning techniques such as federated learning (FL) enable the training of models on effectively larger datasets without data transfer. Recent initiatives have demonstrated that segmentation models trained with FL can achieve performance similar to locally trained models. However, FL is not a fully privacy-preserving technique and privacy-centred attacks can disclose confidential patient data. Thus, supplementing FL with privacy-enhancing technologies (PTs) such as differential privacy (DP) is a requirement for clinical applications in a multi-institutional setting. The a…
Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；University of Freiburg（弗莱堡大学）；University of Bonn（波恩大学）
Given the high incidence and effective treatment options for liver diseases, they are of great socioeconomic importance. One of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment is liver segmentation. Recent advances in deep learning have demonstrated encouraging results for automatic liver segmentation. Despite this, their success depends primarily on the availability of an annotated database, which is often not available because of privacy concerns. Federated Learning has been recently proposed as a solution to alleviate these challenges by trainin…
