Chest Computational Tomography (CT) scans present low cost, speed and objectivity for COVID-19 diagnosis and deep learning methods have shown great promise in assisting the analysis and interpretation of these images. Most hospitals or countries can train their own models using in-house data, however empirical evidence shows that those models perform poorly when tested on new unseen cases, surfacing the need for coordinated global collaboration. Due to privacy regulations, medical data sharing between hospitals and nations is extremely difficult. We propose a GAN-augmented federated learning model, dubbed ST-FL (Style Transfer Federated Learning), for COVID-19 image segmentation. Federated learning (FL) permits a centralised model to be learned in a secure manner from heterogeneous datasets located in disparate private data silos. We demonstrate that the widely varying data quality on FL client nodes leads to a sub-optimal centralised FL model for COVID-19 chest CT image segmentation. ST-FL is a novel FL framework that is robust in the face of highly variable data quality at client nodes. The robustness is achieved by a denoising CycleGAN model at each client of the federation that maps arbitrary quality images into the same target quality, counteracting the severe data variability evident in real-world FL use-cases. Each client is provided with the target style, which is the same for all clients, and trains their own denoiser. Our qualitative and quantitative results suggest that this FL model performs comparably to, and in some cases better than, a model that has centralised access to all the training data.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Hainan University（海南大学）；University of Macau（澳门大学）
AI plays an important role in COVID-19 identification. Computer vision and deep learning techniques can assist in determining COVID-19 infection with Chest X-ray Images. However, for the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission. Collecting such training data was a major challenge. To a certain extent, this has caused a lack of sufficient data samples when performing deep learning approaches to detect COVID-19. Federated Learning is an available way to address this issue. It can effective…
Deep learning is effective in diagnosing COVID-19 and requires a large amount of data to be effectively trained. Due to data and privacy regulations, hospitals generally have no access to data from other hospitals. Federated learning (FL) has been used to solve this problem, where it utilizes a distributed setting to train models in hospitals in a privacy-preserving manner. Deploying FL is not always feasible as it requires high computation and network communication resources. This paper evaluates five FL algorithms' performance and resource efficiency for Covid-19 detection. A decentralized s…
Coronavirus (COVID-19) has shown an unprecedented global crisis by the detrimental effect on the global economy and health. The number of COVID-19 cases has been rapidly increasing, and there is no sign of stopping. It leads to a severe shortage of test kits and accurate detection models. A recent study demonstrated that the chest X-ray radiography outperformed laboratory testing in COVID-19 detection. Therefore, using chest X-ray radiography analysis can help to screen suspected COVID-19 cases at an early stage. Moreover, the patient data is sensitive, and it must be protected to avoid reveal…
University of Electronic Science and Technology of China（电子科技大学）
With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are major concerns for training a global deep learning model. This paper propos…
To address COVID-19 healthcare challenges, we need frequent sharing of health data, knowledge and resources at a global scale. However, in this digital age, data privacy is a big concern that requires the secure embedding of privacy assurance into the design of all technological solutions that use health data. In this paper, we introduce differential privacy by design (dPbD) framework and discuss its embedding into the federated machine learning system. To limit the scope of our paper, we focus on the problem scenario of COVID-19 imaging data privacy for disease diagnosis by computer vision an…
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia
King Abdul Aziz University（阿卜杜勒阿齐兹国王大学）；School of Data Science and Technology, Heilongjiang University（黑龙江大学数据科学与技术学院）；Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）
University of Electronic Science and Technology of China（电子科技大学）；Macau University of Science and Technology（澳门科技大学）；Southwest Jiaotong University（西南交通大学）；China Telecommunications Corporation（中国电信集团有限公司）
Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training the model in a single organization, which is most common weakness due to the privacy and security of raw data communication. To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models withou…
China University of Petroleum (East China)（中国石油大学（华东））；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Southern University of Science and Technology（南方科技大学）
Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients' privacy. This causes the issue of insufficient datasets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received updates of local models trained by clients without exchanging clients' local data. Nevertheless…
The outbreak of COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. To aid and accelerate the diagnosis process, automatic diagnosis of COVID-19 via deep learning models has recently been explored by researchers across the world. While different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19, the data itself is still scarce due to patient privacy concerns. Federated Learning (FL) is a natural solution because it allows different organizations to cooperatively learn an effective deep learning model without sharing…
Federated learning (FL) aided health diagnostic models can incorporate data from a large number of personal edge devices (e.g., mobile phones) while keeping the data local to the originating devices, largely ensuring privacy. However, such a cross-device FL approach for health diagnostics still imposes many challenges due to both local data imbalance (as extreme as local data consists of a single disease class) and global data imbalance (the disease prevalence is generally low in a population). Since the federated server has no access to data distribution information, it is not trivial to solv…
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Information Technology University (ITU) Punjab（旁遮普信息技术大学（ITU））
Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the …
University of Calcutta（加尔各答大学）；DeepTek Inc（DeepTek公司）
While developing artificial intelligence (AI)-based algorithms to solve problems, the amount of data plays a pivotal role - large amount of data helps the researchers and engineers to develop robust AI algorithms. In the case of building AI-based models for problems related to medical imaging, these data need to be transferred from the medical institutions where they were acquired to the organizations developing the algorithms. This movement of data involves time-consuming formalities like complying with HIPAA, GDPR, etc.There is also a risk of patients' private data getting leaked, compromisi…
University of Stavanger（斯塔万格大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Old Dominion University（奥尔多明尼昂大学）；Norwegian University of Science and Technology（挪威科技大学）
Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applica…
Qingdao University（青岛大学）；Jilin University（吉林大学）；Kennesaw State University（肯尼索州立大学）；Georgia State University（佐治亚州立大学）
In this work, we propose a collaborative city digital twin based on FL, a novel paradigm that allowing multiple city DT to share the local strategy and status in a timely manner. In particular, an FL central server manages the local updates of multiple collaborators (city DT), provides a global model which is trained in multiple iterations at different city DT systems, until the model gains the correlations between various response plan and infection trend. That means, a collaborative city DT paradigm based on FL techniques can obtain knowledge and patterns from multiple DTs, and eventually es…
Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification
Institute of Exact and Technological Sciences, Federal University of Viçosa - UFV, Rio Paranaíba-MG, Brazil（精确与技术科学研究所，弗拉维亚联邦大学-UFV，里奥帕拉纳伊巴-MG，巴西）；Department of Computing, Federal University of São Carlos, São Carlos-SP, Brazil（计算系，萨o卡洛斯联邦大学，萨o卡洛斯-SP，巴西）
University of Washington（华盛顿大学）；Microsoft Research（微软研究院）；Fudan University（复旦大学）
The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic. In health-related machine learning applications the ability to learn predictive models without data leaving a private device is attractive, especially when these data might contain features (e.g., photographs or videos of the body) that make identifying a subject trivial and/or the training data volume is large (e.g., uncompressed video). Camera-based remote physiological sensing facilitates scalable and low-cost measurement, but is a prime example of a…
Manipal Institute of Technology（马尼帕尔理工学院）；Carnegie Mellon University（卡内基梅隆大学）
The amount of data, manpower and capital required to understand, evaluate and agree on a group of symptoms for the elementary prognosis of pandemic diseases is enormous. In this paper, we present FedPandemic, a novel noise implementation algorithm integrated with cross-device Federated learning for Elementary symptom prognosis during a pandemic, taking COVID-19 as a case study. Our results display consistency and enhance robustness in recovering the common symptoms displayed by the disease, paving a faster and cheaper path towards symptom retrieval while also preserving the privacy of patient'…
Technische Universität Darmstadt（达姆施塔特工业大学）；Universitätsklinikum Frankfurt（法兰克福大学医院）
Federated Learning is the most promising way to train robust Deep Learning models for the segmentation of Covid-19-related findings in chest CTs. By learning in a decentralized fashion, heterogeneous data can be leveraged from a variety of sources and acquisition protocols whilst ensuring patient privacy. It is, however, crucial to continuously monitor the performance of the model. Yet when it comes to the segmentation of diffuse lung lesions, a quick visual inspection is not enough to assess the quality, and thorough monitoring of all network outputs by expert radiologists is not feasible. In…
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multi-stakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we pre…
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for computer-assisted diagnosis. However, there are two most significant challenges in medical image classification with the help of deep learning and neural networks, one of them is the difficulty of acquiring enough samples, which may lead to model overfitting. Privacy concerns mainl…
Scuola Normale Superiore Pisa（比萨高等师范学校）；IIT-CNR（意大利国家研究委员会信息科学与技术研究所）
Federated learning (FL) is proving to be one of the most promising paradigms for leveraging distributed resources, enabling a set of clients to collaboratively train a machine learning model while keeping the data decentralized. The explosive growth of interest in the topic has led to rapid advancements in several core aspects like communication efficiency, handling non-IID data, privacy, and security capabilities. However, the majority of FL works only deal with supervised tasks, assuming that clients' training sets are labeled. To leverage the enormous unlabeled data on distributed edge devi…
Department of Medical Data Science（医学数据科学系）；Wakayama Medical University（福冈医疗大学）；Department of Biomedical Sciences and Informatics（生物医学科学与信息学系）；Doshisha University（多摩大学）；Department of Biostatistics（生物统计学系）
Hainan University（海南大学）；University of Macao（澳门大学）；The Hong Kong Polytechnic University（香港理工大学）
Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every r…
Federated Learning (FL) is a distributed machine learning paradigm where data is distributed among clients who collaboratively train a model in a computation process coordinated by a central server. By assigning a weight to each client based on the proportion of data instances it possesses, the rate of convergence to an accurate joint model can be greatly accelerated. Some previous works studied FL in a Byzantine setting, in which a fraction of the clients may send arbitrary or even malicious information regarding their model. However, these works either ignore the issue of data unbalancedness…
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT
School of Medicine, Stanford University（斯坦福大学医学院）；Insight Centre for Data Analytics, University College Dublin（都柏林大学学院洞察数据分析中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Koç University School of Medicine（科克大学医学院）；Amiralam Hospital, Tehran University of Medical Sciences（德黑兰医科大学阿米拉拉姆医院）；Unity Health Toronto, St. Michael’s Hospital and University of Toronto（多伦多联合健康中心、圣迈克尔医院与多伦多大学）；Amazon Web Services（亚马逊网络服务）；Universidade Federal de São Paulo（圣保罗联邦大学）；Fleni（弗莱尼）；Radiology Department Centre International Carthage Médicale（迦太基国际医疗中心放射科）；Hospital Israelita Albert Einstein（以色列裔爱因斯坦医院）；Kyungpook National University Hospital（庆北国立大学医院）；St. Joseph’s Hospital and Medical Center（圣约瑟夫医院与医疗中心）；Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences（伊朗医科大学拉贾伊心血管医疗与研究中心）；Henry Ford Health System（亨利福特医疗系统）；Guilan University of Medical Sciences（吉兰医科大学）；Ann & Robert H. Lurie Children's Hospital of Chicago（芝加哥安与罗伯特·H·卢里儿童医院）
Label-shift robust federated feature screening for high-dimensional classification
Center for Applied Statistics and School of Statistics Renmin University of China（应用统计中心和中国人民大学统计学院）；School of Mathematics and Statistics Xi’an Jiaotong University（西安交通大学数学与统计学院）；Department of Mathematics and Fundamental Research Peng Cheng Laboratory School of Mathematics and Statistics Xi’an Jiaotong University（数学与基础研究部鹏城实验室数学与统计学院）
University of Amsterdam（阿姆斯特丹大学）；Australian National University（澳大利亚国立大学）；University of Sydney（悉尼大学）；N1 Analytics（N1分析公司）；Data61（Data61数据科学研究中心）
Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem of federated learning over vertically partitioned data includes a crucial upstream issue: entity resolution, i.e. finding the correspondence between the rows of the datasets. It is well known that entity resolution, just like learning, is mistake-prone in the real world. Despite the importance of the problem, there has been no formal assessment of how errors in entity resolution impact learning. In this paper, we pr…
Federated learning (FL) is a technique that trains machine learning models from decentralized data sources. We study FL under local notions of privacy constraints, which provides strong protection against sensitive data disclosures via obfuscating the data before leaving the client. We identify two major concerns in designing practical privacy-preserving FL algorithms: communication efficiency and high-dimensional compatibility. We then develop a gradient-based learning algorithm called \emph{sqSGD} (selective quantized stochastic gradient descent) that addresses both concerns. The proposed al…
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；University of California Santa Cruz（加州大学圣克鲁兹分校）
As the rapid spread of coronavirus disease (COVID-19) worldwide, chest X-ray radiography has also been used to detect COVID-19 infected pneumonia and assess its severity or monitor its prognosis in the hospitals due to its low cost, low radiation dose, and wide accessibility. However, how to more accurately and efficiently detect COVID-19 infected pneumonia and distinguish it from other community-acquired pneumonia remains a challenge. In order to address this challenge, we in this study develop and test a new computer-aided diagnosis (CAD) scheme. It includes several image pre-processing algo…
Nvidia Corporation（英伟达公司）；National Cancer Institute, NIH（美国国立卫生研究院国家癌症研究所）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Leidos Biomedical Research, Inc.（Leidos生物医学研究公司）；NIH Clinical Center（美国国立卫生研究院临床中心）；National Institutes of Health（美国国立卫生研究院）
The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. In this work, we attempt to find …
Deakin University（迪肯大学）；Pusan National University（釜山国立大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；University of New South Wales（新南威尔士大学）；The University of Sydney（悉尼大学）；Memorial University（纪念大学）
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing …
University of Electronic Science and Technology of China（电子科技大学）；École de Technologie Supérieure（高等技术学院）；Carleton University（卡尔顿大学）
With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To t…
University of Auckland（奥克兰大学）；Lehigh University（利哈伊大学）；Macquarie University（麦考瑞大学）
Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows multiple clients to jointly train a model without sharing their private data. Recently, many studies have shown that FL is vulnerable to membership inference attacks (MIAs) that can distinguish the training members of the given model from the non-members. However, existing MIAs ignore the source of a training member, i.e., the information of which client owns the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients. The leakage of source…
One of the most contentious areas of research in Medical Image Preprocessing is 3D CT-scan. With the rapid spread of COVID-19, the function of CT-scan in properly and swiftly diagnosing the disease has become critical. It has a positive impact on infection prevention. There are many tasks to diagnose the illness through CT-scan images, include COVID-19. In this paper, we propose a method that using a Stacking Deep Neural Network to detect the Covid 19 through the series of 3D CT-scans images . In our method, we experiment with two backbones are DenseNet 121 and ResNet 101. This method achieves…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Kim Jaechul Graduate School of AI（金在彻人工智能研究生院）
Federated learning, which shares the weights of the neural network across clients, is gaining attention in the healthcare sector as it enables training on a large corpus of decentralized data while maintaining data privacy. For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals. Unfortunately, the exchange of the weights quickly consumes the network bandwidth if highly expressive network architecture is employed. So-called split learning partially solves this problem by dividing a neural…
Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning
Department of Computer Science and Engineering, European University of Bangladesh（欧洲 bangladesh 大学 计算机科学与工程系）；Department of Computer Science and Engineering , BRAC University,Dhaka, Bangladesh（brac 大学 计算机科学与工程系）；Department of Computer Science and Engineering , Daffodil International University, Dhaka, Bangladesh（daffodil 国际大学 计算机科学与工程系）；Department of Institute of Information Technology, University of Dhaka, Bangladesh（达卡大学 信息科技学院）；Department of Computer Science and Engineering , BRAC University,Dhaka（brac 大学 计算机科学与工程系）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（ochsner 成果研究中心，ochsner 研究）
Melbourne Data Analytics Platform（墨尔本数据分析平台）；University of Melbourne（墨尔本大学）
Improving automated analysis of medical imaging will provide clinicians more options in providing care for patients. The 2023 AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides an opportunity to test and refine machine learning methods for detecting the presence and severity of COVID-19 in patients from CT scans. This paper presents version 2 of Cov3d, a deep learning model submitted in the 2022 competition. The model has been improved through a preprocessing step which segments the lungs in the CT scan and crops the input to this region. It …
Universidad de Antioquia（安蒂奥基亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Universidad Politécnica de Madrid（马德里理工大学）
Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests, but also to provide complementary evidence about the presence and severity of the disease. However, the procedure is not free of errors, and the interpretation of the chest X-Ray is only restricted to radiologists due to its complexity. With the long term goal to provide new evidence for the diagnosis, this paper presents an evaluation of different m…
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；University of Oxford（牛津大学）；Oxford, UK（英国牛津（这里仅保留城市和国家，实际机构为牛津大学））
A label-efficient paradigm in computer vision is based on self-supervised contrastive pre-training on unlabeled data followed by fine-tuning with a small number of labels. Making practical use of a federated computing environment in the clinical domain and learning on medical images poses specific challenges. In this work, we propose FedMoCo, a robust federated contrastive learning (FCL) framework, which makes efficient use of decentralized unlabeled medical data. FedMoCo has two novel modules: metadata transfer, an inter-node statistical data augmentation module, and self-adaptive aggregation…
