Abstract: Federated Learning (FL) has emerged as a promising paradigm for collaborative and privacy-preserving model training in medical imaging. However, FL faces major challenges such as data ...
Abstract: Semi-supervised learning methods, compared to fully supervised learning, offer significant potential to alleviate the burden of manual annotations on clinicians. By leveraging unlabeled data ...
ATLANTA — The demolition of the old Atlanta Medical Center, formerly known as Georgia Baptist, is underway. One of the largest machines of its kind is tearing down the hospital, piece by piece. For ...
Recently, machine learning has gained traction in stroke management, prompting the exploration of predictive models for HT. However, systematic evidence on these models is lacking. Objective: In this ...
Add a description, image, and links to the medical-image-fusion topic page so that developers can more easily learn about it.
Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review
Currently, Machine Learning Operations (MLOps), a set of practices designed to deploy and maintain ML models in production, is used in various information technology and industrial settings. However, ...
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