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09/01/2023
Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning
Federated and Continual Learning have emerged as potential paradigms for...
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02/07/2023
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
Access to the proper infrastructure is critical when performing medical ...
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08/05/2022
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
Automatic segmentation of ground glass opacities and consolidations in c...
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08/05/2022
Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
Most continual learning methods are validated in settings where task bou...
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04/17/2022
Continual Hippocampus Segmentation with Transformers
In clinical settings, where acquisition conditions and patient populatio...
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01/26/2022
Improving robustness and calibration in ensembles with diversity regularization
Calibration and uncertainty estimation are crucial topics in high-risk e...
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01/14/2022
Disentanglement enables cross-domain Hippocampus Segmentation
Limited amount of labelled training data are a common problem in medical...
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12/16/2021
Quality monitoring of federated Covid-19 lesion segmentation
Federated Learning is the most promising way to train robust Deep Learni...
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09/03/2021
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
The recent achievements of Deep Learning rely on the test data being sim...
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07/19/2021
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
Deep learning for medical imaging suffers from temporal and privacy-rela...
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07/13/2021
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 po...
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10/21/2020
What is Wrong with Continual Learning in Medical Image Segmentation?
Continual learning protocols are attracting increasing attention from th...
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07/01/2020
