XGBoost is a machine learning algorithm widely used for tabular data modeling. To expand the XGBoost model from single-site learning to multisite collaborative training, NVIDIA has developed Federated XGBoost, an XGBoost plugin for federation learning. It covers vertical collaboration settings to jointly train XGBoost models across decentralized data sources, as well as horizontal histogram-based…
]]>XGBoost is a highly effective and scalable machine learning algorithm widely employed for regression, classification, and ranking tasks. Building on the principles of gradient boosting, it combines the predictions of multiple weak learners, typically decision trees, to produce a robust overall model. XGBoost excels with large datasets and complex data structures, thanks to its efficient…
]]>Federated learning (FL) has become a reality for many real-world applications. It enables multinational collaborations on a global scale to build more robust and generalizable machine learning and AI models. For more information, see Federated learning for predicting clinical outcomes in patients with COVID-19. NVIDIA FLARE v2.0 is an open-source FL SDK that is making it easier for data…
]]>Deep neural networks (DNNs) have been successfully applied to volume segmentation and other medical imaging tasks. They are capable of achieving state-of-the-art accuracy and can augment the medical imaging workflow with AI-powered insights. However, training robust AI models for medical imaging analysis is time-consuming and tedious and requires iterative experimentation with parameter…
]]>Deep Learning in medical imaging has shown great potential for disease detection, localization, and classification within radiology. Deep Learning holds the potential to create solutions that can detect conditions that might have been overlooked and can improve the efficiency and effectiveness of the radiology team. However, for this to happen data scientists and radiologists need to collaborate…
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