The current and future state of AI interpretation of medical images
The Current and Future State of AI Interpretation of Medical Images | New England Journal of
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
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 …
Federated benchmarking of medical artificial intelligence with MedPerf
Medical artificial intelligence (AI) has tremendous potential to advance healthcare by
supporting and contributing to the evidence-based practice of medicine, personalizing …
supporting and contributing to the evidence-based practice of medicine, personalizing …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning
Unsorted retired batteries with varied cathode materials hinder the adoption of direct
recycling due to their cathode-specific nature. The surge in retired batteries necessitates …
recycling due to their cathode-specific nature. The surge in retired batteries necessitates …
OpenFL: the open federated learning library
Objective. Federated learning (FL) is a computational paradigm that enables organizations
to collaborate on machine learning (ML) and deep learning (DL) projects without sharing …
to collaborate on machine learning (ML) and deep learning (DL) projects without sharing …
Fair federated medical image segmentation via client contribution estimation
How to ensure fairness is an important topic in federated learning (FL). Recent studies have
investigated how to reward clients based on their contribution (collaboration fairness), and …
investigated how to reward clients based on their contribution (collaboration fairness), and …
AI in pathology: what could possibly go wrong?
The field of medicine is undergoing rapid digital transformation. Pathologists are now
striving to digitize their data, workflows, and interpretations, assisted by the enabling …
striving to digitize their data, workflows, and interpretations, assisted by the enabling …
Decentralized federated learning: A survey and perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
Challenges and prospects of visual contactless physiological monitoring in clinical study
The monitoring of physiological parameters is a crucial topic in promoting human health and
an indispensable approach for assessing physiological status and diagnosing diseases …
an indispensable approach for assessing physiological status and diagnosing diseases …