36 citations · 199 across the 37 of their papers we have counts for
12 papers · 1 filter
Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models
Yannik Frisch, Moritz Fuchs, Antoine Sanner +7
Cataract surgery is a frequently performed procedure that demands automation and advanced assistance systems. However, gathering and annotating data for training such systems is re…
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
John Kalkhof, Camila González, Anirban Mukhopadhyay
Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentat…
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
Xiangyu Li, Gongning Luo, Kuanquan Wang +31
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores…
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…
Quality monitoring of federated Covid-19 lesion segmentation
Camila Gonzalez, Christian Harder, Amin Ranem +5
Federated Learning is the most promising way to train robust Deep Learning models for the segmentation of Covid-19-related findings in chest CTs. By learning in a decentralized fas…
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…
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation
Camila Gonzalez, Karol Gotkowski, Andreas Bucher +3
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models a…
A survey on shape-constraint deep learning for medical image segmentation
Simon Bohlender, Ilkay Oksuz, Anirban Mukhopadhyay
Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep learning based medical image segmenta…