Search Results for author:
Found 15 papers, 2 papers with code
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
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 scientific machine learning.
The Future of Digital Health with Federated Learning
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 healthcare systems.
Privacy-preserving Federated Brain Tumour Segmentation
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake.
Redefining Ultrasound Compounding: Computational Sonography
By providing a novel paradigm for the acquisition and reconstruction of tracked freehand 3D ultrasound, this work presents the concept of Computational Sonography (CS) to model the directionality of ultrasound information.
BACH: Grand Challenge on Breast Cancer Histology Images
From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images.
Initialize globally before acting locally: Enabling Landmark-free 3D US to MRI Registration
Registration of partial-view 3D US volumes with MRI data is influenced by initialization.
CFCM: Segmentation via Coarse to Fine Context Memory
Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales.
SobolevFusion: 3D Reconstruction of Scenes Undergoing Free Non-Rigid Motion
We present a system that builds 3D models of non-rigidly moving surfaces from scratch in real time using a single RGB-D stream.
Understanding Regularization to Visualize Convolutional Neural Networks
Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years.
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks
Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide.
KillingFusion: Non-Rigid 3D Reconstruction Without Correspondences
We introduce a geometry-driven approach for real-time 3D reconstruction of deforming surfaces from a single RGB-D stream without any templates or shape priors.
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
In future prediction, for example, many distinct outcomes are equally valid.
Deep Active Contours
We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework.
Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests
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 tomography (CT) data.
Total Variation Regularization of Shape Signals
This paper introduces the concept of shape signals, i. e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals.
