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Found 4 papers, 1 papers with code
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness.
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease
We present the first federated learning study on the modality of cardiovascular magnetic resonance (CMR) and use four centers derived from subsets of the M\&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy (HCM).
Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation
Accurately estimating the orientation of pedestrians is an important and challenging task for autonomous driving because this information is essential for tracking and predicting pedestrian behavior.
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation
Training 3D object detectors for autonomous driving has been limited to small datasets due to the effort required to generate annotations.
