Federated Learning In Healthcare Field
Listing Websites about Federated Learning In Healthcare Field
The future of digital health with federated learning
(Just Now) WEBThe applicability and advantages of FL have also been demonstrated in the field of medical imaging, J. & Wang, F. Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270
https://www.nature.com/articles/s41746-020-00323-1
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Federated learning-based AI approaches in smart …
(3 days ago) WEBFederated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385101/
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Federated Learning for Healthcare Domain - Pipeline, …
(8 days ago) WEBThis motivates us to examine the potential and value of federated learning for the healthcare domain. Federated Learning is an advanced distributed learning technique that leverages datasets from various universities …
https://dl.acm.org/doi/10.1145/3533708
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A Comprehensive Survey on Federated Learning …
(9 days ago) WEBAI methods are increasingly used to support experts in the medical field due to their effectiveness in detecting and classifying diseases. A few ML approaches like prediction Ahsan M. A., Al- Fuqaha A., Qadir J. Collaborative federated learning for healthcare: multi-modal COVID- 19 diagnosis at the edge. 2021. arXiv preprint …
https://ncbi.nlm.nih.gov/pmc/articles/PMC9995203/
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Federated machine learning in healthcare: A systematic …
(3 days ago) WEBFederated learning (FL) is a distributed machine learning framework that is gaining traction in view of increasing health data privacy protection needs. By conducting a systematic review of FL applications in healthcare, we identify relevant articles in scientific, engineering, and medical journals in English up to August 31st, 2023.
https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(24)00042-9
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Federated learning: a collaborative effort to achieve …
(3 days ago) WEBHowever, important challenges remain and must be addressed before federated learning is optimally able to build AI models. Further, because of the novelty of federated learning in medical imaging AI, this topic has the potential to inspire and attract researchers, whose work will be necessary to advance the field forward.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779924/
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Federated Learning for Healthcare: A Comprehensive …
(9 days ago) WEBBy leveraging federated learning, healthcare institutions can harness the collective power of their data while upholding privacy and data security standards, ultimately leading to more effective and data-driven healthcare solutions. There is a lack of benchmark datasets, which is especially problematic in the medical field. To evaluate the
https://www.mdpi.com/2673-4591/59/1/230
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Federated Learning for Healthcare Informatics Journal of …
(Just Now) WEBMedical treatment itself is a very professional and accurate field. Medical devices in hospitals have incomparable advantages over wearable devices. Federated learning for healthcare informatics. arXiv:1911.06270. Xu J, Xu Z, Walker P, Wang F (2020) Federated patient hashing. In: AAAI, pp 6486–6493. Yang Q, Liu Y, Chen T, …
https://link.springer.com/article/10.1007/s41666-020-00082-4
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A Review of Medical Federated Learning: Applications in Oncology …
(1 days ago) WEBFederated Learning, while still a new research field, has already demonstrated its potential use to support a distributed learning setup for healthcare. While the general field of Federated Learning research is very active with a focus on improving model aggregation and efficient communication between nodes, model and data privacy …
https://link.springer.com/chapter/10.1007/978-3-031-08999-2_1
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Federated Learning in a Medical Context: A Systematic Literature …
(7 days ago) WEBAlthough federated reinforcement learning is an active sub-field of FL research, reinforcement learning and its applications are quite separate from un- and supervised learning, using very different underlying concepts. In addition, we found no paper looking into federated reinforcement learning for healthcare; thus, we omitted this area of
https://dl.acm.org/doi/fullHtml/10.1145/3412357
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Federated Learning for Healthcare Applications IEEE Journals
(5 days ago) WEBDue to the fast advancement of artificial intelligence (AI), centralized-based models have become critical for healthcare tasks like in medical image analysis and human behavior recognition. Although these models exhibit suitable performance, they are frequently constrained by privacy concerns. To attenuate this, a centralized learning …
https://ieeexplore.ieee.org/document/10288131
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Topic Spotlight: Federated Learning Medical School
(5 days ago) WEBTopic Spotlight: Federated Learning. May 20, 2024. Read Time: 4 minutes. Federated learning is an important concept when it comes to developing and validating artificial intelligence (AI) models for clinical applications. CLHSS and our programs are conducting key projects related to federated learning. We want to describe what …
https://med.umn.edu/clhss/news/topic-spotlight-federated-learning
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[2111.08834] Federated Learning for Smart Healthcare: A Survey
(2 days ago) WEBFederated Learning for Smart Healthcare: A Survey. Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang. Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence …
https://arxiv.org/abs/2111.08834
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Federated Learning for Healthcare Informatics - PMC
(3 days ago) WEBFederated learning might be the tool to enable large-scale representative ML of EHR data and we discuss many studies which demonstrate this fact below. Federated learning is a viable method to connect EHR data from medical institutions, allowing them to share their experiences, and not their data, with a guarantee of privacy …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659898/
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Unified fair federated learning for digital healthcare
(7 days ago) WEBFederated learning (FL) is a promising approach for healthcare institutions to train high-quality medical models collaboratively while protecting sensitive data privacy. However, FL models encounter fairness issues at diverse levels, leading to performance disparities across different subpopulations. To address this, we propose Federated
https://www.sciencedirect.com/science/article/pii/S2666389923003148
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Federated Learning Systems for Healthcare: Perspective and
(1 days ago) WEBTensorFlow Federated. TFF is an open-source framework for experimenting with federated machine learning and other computations on decentralized datasets. It enables developers to experiment with novel algorithms and also lets them simulate existing FL algorithms on their models and data. TensorFlow-Encrypted.
https://link.springer.com/chapter/10.1007/978-3-030-70604-3_6
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Open problems in medical federated learning Emerald Insight
(Just Now) WEBIn Section 1, we describe the concept of federated learning and specific structures in medical environments. In Section 3, how federated learning is applied to the medical fields is described. Section 4 contains open problems in medical federated learning and existing solutions in the following order: data/system heterogeneity, client
https://www.emerald.com/insight/content/doi/10.1108/IJWIS-04-2022-0080/full/html
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[2405.06784] Open Challenges and Opportunities in Federated …
(2 days ago) WEBThis survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) for advancing biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, …
https://arxiv.org/abs/2405.06784
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The Benefits of AI in Healthcare IBM
(7 days ago) WEBBetter machine learning (ML) algorithms, more access to data, cheaper hardware, and the availability of 5G have contributed to the increasing application of AI in the healthcare industry, accelerating the pace of change. AI and ML technologies can sift through enormous volumes of health data—from health records and clinical studies to …
https://www.ibm.com/think/insights/ai-healthcare-benefits
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New AI model uses federated learning for multi-organ …
(4 days ago) WEBMore information: Soopil Kim et al, Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets, Medical Image Analysis (2024). DOI: 10.1016/j.media
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Federated Learning in Healthcare with Unsupervised and Semi
(2 days ago) WEBAbstract. The federated paradigm has made possible the development of techniques capable of solving advanced problems in the healthcare field through the protection of data privacy. However, most existing research is centered around supervised methods and real world data tends to be unevenly distributed and scarcely labelled.
https://link.springer.com/chapter/10.1007/978-3-031-42935-4_15
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The relative effects of direct and indirect written corrective …
(3 days ago) WEBThe current study investigated the relative effects of direct and indirect written corrective feedback (WCF) on the learning of regular past tense, and the extent to which learning outcomes were related to individual variation in …
https://journals.sagepub.com/doi/full/10.1177/13621688241251551
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Federated learning-based AI approaches in smart healthcare: …
(Just Now) WEBFederated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in …
https://link.springer.com/article/10.1007/s10586-022-03658-4
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Applications of Federated Learning in Mobile Health: Scoping …
(4 days ago) WEBAdditionally, machine learning (ML) techniques have been used to enhance diagnostic precision and facilitate remote, fine-grained, and high-quality health care in the mHealth field [3-5]. However, the traditional approach of training ML models requires centralized datasets, where a central server has access to the data of all patients.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186185/
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Top AI Tools for Genomics, Drug Discovery, And Machine Learning
(2 days ago) WEBArtificial intelligence's incorporation has become a paradigm shifter in the dynamic field of biological research. Artificial intelligence (AI) powered tools are revolutionizing bioinformatics, helping to speed up drug development and decipher genomic puzzles. Here are the top AI tools for genomics, drug discovery, and machine learning …
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Smart client selection strategies for enhanced federated learning …
(Just Now) WEBFederated Learning (FL) trains AI models in healthcare without sharing patient data. FL computes client models locally and combines them to create a global model. However, involving all clients is impractical due to resource limitations. Random selection of a subset of clients in each FL round can pose challenges for resource-limited devices, …
https://link.springer.com/article/10.1007/s11042-024-19403-5
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Call for Applications: Postgraduate Training Scheme in …
(5 days ago) WEBPostgraduate researchers committed to addressing infectious diseases of poverty and shaping the future of global health can now apply for a postgraduate fellowship.TDR, the Special Programme for Research and Training in Tropical Diseases, in its mission to empower the next generation of researchers and leaders from low- and …
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