Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models
Federated Learning (FL) could solve the challenges of training AI models on large datasets
for medical imaging due to data privacy and ownership concerns by allowing collaborative …
for medical imaging due to data privacy and ownership concerns by allowing collaborative …
Multi-diseases classification from chest-x-ray: A federated deep learning approach
Data plays a vital role in deep learning model training. In large-scale medical image
analysis, data privacy and ownership make data gathering challenging in a centralized …
analysis, data privacy and ownership make data gathering challenging in a centralized …
A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications
Recent advances in deep learning have shown many successful stories in smart healthcare
applications with data-driven insight into improving clinical institutions' quality of care …
applications with data-driven insight into improving clinical institutions' quality of care …
MERGE: A model for multi-input biomedical federated learning
Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a
fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic …
fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic …
Framework Construction of an Adversarial Federated Transfer Learning Classifier
H Yi, T Bie, T Yan - arXiv preprint arXiv:2211.04734, 2022 - arxiv.org
As the Internet grows in popularity, more and more classification jobs, such as IoT, finance
industry and healthcare field, rely on mobile edge computing to advance machine learning …
industry and healthcare field, rely on mobile edge computing to advance machine learning …
Federated learning for COVID-19 screening from Chest X-ray images
Today, the whole world is facing a great medical disaster that affects the health and lives of
the people: the COVID-19 disease, colloquially known as the Corona virus. Deep learning is …
the people: the COVID-19 disease, colloquially known as the Corona virus. Deep learning is …
Federated Learning for Medical Imaging Segmentation via Dynamic Aggregation on Non-IID Data Silos
L Yang, J He, Y Fu, Z Luo - Electronics, 2023 - mdpi.com
A large number of mobile devices, smart wearable devices, and medical and health sensors
continue to generate massive amounts of data, making edge devices' data explode and …
continue to generate massive amounts of data, making edge devices' data explode and …
Federated learning for medical imaging radiology
MH Rehman, W Hugo Lopez Pinaya… - The British Journal of …, 2023 - academic.oup.com
Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …
Identification of Kidney Disorders in Decentralized Healthcare Systems through Federated Transfer Learning
V Vekaria, R Gandhi, B Chavarkar, H Shah… - Procedia Computer …, 2024 - Elsevier
This research introduces a pioneering approach to address the intricate challenge of
identifying kidney abnormalities in medical imaging. By synergizing the strengths of transfer …
identifying kidney abnormalities in medical imaging. By synergizing the strengths of transfer …
An experimental study of data heterogeneity in federated learning methods for medical imaging
L Qu, N Balachandar, DL Rubin - arXiv preprint arXiv:2107.08371, 2021 - arxiv.org
Federated learning enables multiple institutions to collaboratively train machine learning
models on their local data in a privacy-preserving way. However, its distributed nature often …
models on their local data in a privacy-preserving way. However, its distributed nature often …