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Found 5 papers, 0 papers with code
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
Our results underscore the value of efforts needed to translate FL into real-world applications by demonstrating advantageous performance over alternatives, and emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings.
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation.
Quality monitoring of federated Covid-19 lesion segmentation
It is, however, crucial to continuously monitor the performance of the model.
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic.
M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning
M3d-CAM is an easy to use library for generating attention maps of CNN-based PyTorch models improving the interpretability of model predictions for humans.
