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It is concluded that the incorporation of routinely-collected clinical and demographic data is a promising way to improve the performance of lung ultrasound based automatic classification.
CLIMB is introduced, a benchmark in which a doctor model interviews a simulated patient to recover a ground truth set of co-occurring clinical conditions, and a theoretical reference model of single-hypothesis tracking is formalised with a theoretical reference model of single-hypothesis tracking.
FedMoDN, a novel federated modular neural network architecture for collaborative learning across all features of an IIO distributed dataset, allowing healthcare facilities to use the full complement of their features without sharing, discarding, or imputing any data, is introduced.
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