Search Results for author:
Found 29 papers, 11 papers with code
Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation
To this end, we propose a combination of a global controller leveraging voxel mask segmentations to provide boundary conditions for vessels of interest to a local, iterative vessel segmentation model.
Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow
This paper presents a method for finding a sparse representation of Barron functions.
SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks
Hence, SIRE can be trained with arbitrarily oriented vessels with varying radii to generalise to vessels with a wide range of calibres and tortuosity.
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI
The application of deep learning models to large-scale data sets requires means for automatic quality assurance.
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning
We propose a data augmentation strategy, named DFM-X, that leverages knowledge about frequency shortcuts, encoded in Dominant Frequencies Maps computed for image classification models.
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space.
What do neural networks learn in image classification? A frequency shortcut perspective
Our results demonstrate that NNs tend to find simple solutions for classification, and what they learn first during training depends on the most distinctive frequency characteristics, which can be either low- or high-frequencies.
Embeddings between Barron spaces with higher order activation functions
Moreover, the Barron spaces associated with the $\operatorname{RePU}_s$ have a hierarchical structure similar to the Sobolev spaces $H^m$.
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations
In this work, we focus on a limitation of neural network-based atlas building and statistical latent modeling methods, namely that they either are (i) resolution dependent or (ii) disregard any data/problem-specific geometry needed for proper mean-variance analysis.
A Survey on the Robustness of Computer Vision Models against Common Corruptions
The performance of computer vision models are susceptible to unexpected changes in input images, known as common corruptions (e. g. noise, blur, illumination changes, etc.
Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression
We represent the AAA wall over time as the zero-level set of a signed distance function (SDF), estimated by a multilayer perception that operates on space and time.
SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets
We demonstrate how to construct an SE(3)-equivariant GNN that is independent of the spatial orientation of the input mesh and show how this reduces the necessary amount of training data compared to a baseline neural network.
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice.
Duality for Neural Networks through Reproducing Kernel Banach Spaces
Recently, Barron spaces have been used to prove bounds on the generalisation error for neural networks.
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data.
Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL).
Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling
This allows us to model complex vascular structures with a representation that is implicit, continuous, light-weight, and easy to integrate with deep learning algorithms.
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning
The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis.
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD.
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Inspection and maintenance are two crucial aspects of industrial pipeline plants.
Autonomous Navigation
Hierarchical Reinforcement Learning
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Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life.
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Results show that the novel framework can efficiently learn low-dimensional and interpretable state and action representations and the optimal latent policy.
Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations
In this work, we introduce deep learning autoencoders to discover coordinate transformations that capture the underlying parametric dependence of a dynamical system in terms of its canonical normal form, allowing for a simple representation of the parametric dependence and bifurcation structure.
Low Dimensional State Representation Learning with Reward-shaped Priors
We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space.
Learned SVD: solving inverse problems via hybrid autoencoding
Our world is full of physics-driven data where effective mappings between data manifolds are desired.
Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition
However, for an effective use in image segmentation and shape decomposition, a clear interpretation of the spectral response regarding size and intensity scales is needed but lacking in current approaches.
Multiscale Segmentation via Bregman Distances and Nonlinear Spectral Analysis
For this we generalize a variational segmentation model based on total variation using Bregman distances to construct an inverse scale space.
Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs
Our approach, based on experimental design, is to view data collection as a bi-level optimization problem where the inner problem is the ranking problem and the outer problem is to identify data which maximizes the informativeness of the ranking.
