3.1k citations · 3.3k across the 11 of their papers we have counts for
12 papers
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Boris Bonev, Thorsten Kurth, Christian Hundt +4
Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scie…
The Future of Digital Health with Federated Learning
Nicola Rieke, Jonny Hancox, Wenqi Li +14
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern…
Privacy-preserving Federated Brain Tumour Segmentation
Wenqi Li, Fausto Milletarì, Daguang Xu +8
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning al…
Redefining Ultrasound Compounding: Computational Sonography
Rüdiger Göbl, Diana Mateus, Christoph Hennersperger +2
Freehand three-dimensional ultrasound (3D-US) has gained considerable interest in research, but even today suffers from its high inter-operator variability in clinical practice. Th…
Phenomenological Inclusion of Alternative Dispersion Relations to the Teukolsky Equation and its Application to Bounding the Graviton Mass with Gravitational-wave Measurements
Ka-Wai Chung, Tjonnie Guang Feng Li
Existing constraints on the graviton mass from gravitational-wave detections rely on the phase difference developed between different frequencies during the propagation. Effects on…
Initialize globally before acting locally: Enabling Landmark-free 3D US to MRI Registration
Julia Rackerseder, Maximilian Baust, Rüdiger Göbl +2
Registration of partial-view 3D US volumes with MRI data is influenced by initialization. The standard of practice is using extrinsic or intrinsic landmarks, which can be very tedi…
CFCM: Segmentation via Coarse to Fine Context Memory
Fausto Milletari, Nicola Rieke, Maximilian Baust +2
Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales. Although yield…
Understanding Regularization to Visualize Convolutional Neural Networks
Maximilian Baust, Florian Ludwig, Christian Rupprecht +2
Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years. Being based on noisy gradients o…
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks
Matthias Kohl, Christoph Walz, Florian Ludwig +2
Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densel…
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
Christian Rupprecht, Iro Laina, Robert DiPietro +4
Many prediction tasks contain uncertainty. In some cases, uncertainty is inherent in the task itself. In future prediction, for example, many distinct outcomes are equally valid. I…
Deep Active Contours
Christian Rupprecht, Elizabeth Huaroc, Maximilian Baust +1
We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework. We train a class-specific convolutional…
Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests
Kanishka Sharma, Loic Peter, Christian Rupprecht +5
This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed t…