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Found 8 papers, 1 papers with code
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.
Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing.
Quality monitoring of federated Covid-19 lesion segmentation
It is, however, crucial to continuously monitor the performance of the model.
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
In this work, we evaluate OoD Generalization solutions for the problem of hippocampus segmentation in MR data using both fully- and semi-supervised training.
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability.
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.
What is Wrong with Continual Learning in Medical Image Segmentation?
Continual learning protocols are attracting increasing attention from the medical imaging community.
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.
