21 citations · 53 across the 15 of their papers we have counts for
22 papers
Federated Stain Normalization for Computational Pathology
Nicolas Wagner, Moritz Fuchs, Yuri Tolkach +1
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology.…
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
Amin Ranem, John Kalkhof, Caner Özer +2
While machine learning approaches perform well on their training domain, they generally tend to fail in a real-world application. In cardiovascular magnetic resonance imaging (CMR)…
Continual Hippocampus Segmentation with Transformers
Amin Ranem, Camila González, Anirban Mukhopadhyay
In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most e…
Disentanglement enables cross-domain Hippocampus Segmentation
John Kalkhof, Camila González, Anirban Mukhopadhyay
Limited amount of labelled training data are a common problem in medical imaging. This makes it difficult to train a well-generalised model and therefore often leads to failure in…
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
Antoine Sanner, Camila Gonzalez, Anirban Mukhopadhyay
The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distr…
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
Marius Memmel, Camila Gonzalez, Anirban Mukhopadhyay
Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability. To still obtain viable models, continual learning aims to train in se…