Collaborating efficiently on medical imaging presents hurdles stemming from data privacy concerns and collaboration barriers. In response to these challenges, federated learning emerges as a decentralized machine learning method showing considerable promise. Its notable advantage lies in preserving data privacy robustly, enabling collaborative efforts without compromising sensitive medical information confidentiality. This study meticulously examines federated learning's application in medical imaging, thoroughly assessing its strengths and limitations. Our experimental results show that federated learning has potential in the field of medical images, achieving cooperative improvement in model performance while protecting data privacy.
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A Machine Learning Approach for Medical Imaging Evaluation
Semantic Scholar · Medicine · 2024
Abstract
Collaborating efficiently on medical imaging presents hurdles stemming from data privacy concerns and collaboration barriers. In response to these challenges, federated learning emerges as a decentralized machine learning method showing considerable promise. Its notable advantage lies in preserving data privacy robustly, enabling collaborative efforts without compromising sensitive medical information confidentiality. This study meticulously examines federated learning's application in medical imaging, thoroughly assessing its strengths and limitations. Our experimental results show that federated learning has potential in the field of medical images, achieving cooperative improvement in model performance while protecting data privacy.