77 citations · 110 across the 23 of their papers we have counts for
10 papers · 1 filter
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
Samuele Papa, Riccardo Valperga, David Knigge +4
Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, a number of works h…
Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
Joren Brunekreef, Eric Marcus, Ray Sheombarsing +2
Image segmentation algorithms can be understood as a collection of pixel classifiers, for which the outcomes of nearby pixels are correlated. Classifier models can be calibrated us…
Joint Supervised and Self-supervised Learning for MRI Reconstruction
George Yiasemis, Nikita Moriakov, Clara I. Sánchez +2
Magnetic Resonance Imaging (MRI) represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully-sampled -space…
Deep Cardiac MRI Reconstruction with ADMM
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Cardiac magnetic resonance imaging is a valuable non-invasive tool for identifying cardiovascular diseases. For instance, Cine MRI is the benchmark modality for assessing the cardi…
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Medical Imaging (MI) tasks, such as accelerated parallel Magnetic Resonance Imaging (MRI), often involve reconstructing an image from noisy or incomplete measurements. This amounts…
Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Samuele Papa, David M. Knigge, Riccardo Valperga +4
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex…
Constrained Empirical Risk Minimization: Theory and Practice
Eric Marcus, Ray Sheombarsing, Jan-Jakob Sonke +1
Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions. This flexibility, however, makes the strict enforcement of cons…
Modelling Long Range Dependencies in D: From Task-Specific to a General Purpose CNN
David M. Knigge, David W. Romero, Albert Gu +5
Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data. In…
On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction
George Yiasemis, Clara I. Sánchez, Jan-Jakob Sonke +1
Acquiring fully-sampled MRI -space data is time-consuming, and collecting accelerated data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling scheme…
Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
Jie Liu, Yanqi Bao, Wenzhe Yin +4
Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples and has made great progress recently. Most of the existing FSS mod…