Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Objective To review and appraise the validity and usefulness of published and preprint
reports of prediction models for prognosis of patients with covid-19, and for detecting people …
reports of prediction models for prognosis of patients with covid-19, and for detecting people …
Federated learning for healthcare domain-pipeline, applications and challenges
M Joshi, A Pal, M Sankarasubbu - ACM Transactions on Computing for …, 2022 - dl.acm.org
Federated learning is the process of developing machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …
distributed across data centers such as hospitals, clinical research labs, and mobile devices …
[HTML][HTML] Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues
Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Review on security of federated learning and its application in healthcare
Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …
progress has been made on many complex medical problems. However, there is a lack of …
Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
Explainable, domain-adaptive, and federated artificial intelligence in medicine
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …
each domain is driven by a growing body of annotated data, increased computational …
[HTML][HTML] Federated learning in medical imaging: Part I: toward multicentral health care ecosystems
E Darzidehkalani, M Ghasemi-Rad… - Journal of the american …, 2022 - Elsevier
With recent developments in medical imaging facilities, extensive medical imaging data are
produced every day. This increasing amount of data provides an opportunity for researchers …
produced every day. This increasing amount of data provides an opportunity for researchers …
FedMix: Mixed supervised federated learning for medical image segmentation
The purpose of federated learning is to enable multiple clients to jointly train a machine
learning model without sharing data. However, the existing methods for training an image …
learning model without sharing data. However, the existing methods for training an image …
Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge
Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19
lesions in chest computed tomography (CT) might play an important role in the monitoring …
lesions in chest computed tomography (CT) might play an important role in the monitoring …
Mechanisms that incentivize data sharing in federated learning
Federated learning is typically considered a beneficial technology which allows multiple
agents to collaborate with each other, improve the accuracy of their models, and solve …
agents to collaborate with each other, improve the accuracy of their models, and solve …