15.1k citations · 36.9k across the 152 of their papers we have counts for
10 papers · 1 filter
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation
Zhuo Su, Max Welling, Matti Pietikäinen +1
Efficiency and robustness are increasingly needed for applications on 3D point clouds, with the ubiquitous use of edge devices in scenarios like autonomous driving and robotics, wh…
Experimental design for MRI by greedy policy search
Tim Bakker, Herke van Hoof, Max Welling
In today's clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain. Currently, the construction of these s…
Guided Variational Autoencoder for Disentanglement Learning
Zheng Ding, Yifan Xu, Weijian Xu +4
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement lear…
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling +4
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-t…
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
Jasper Linmans, Jim Winkens, Bastiaan S. Veeling +2
We propose a semantic segmentation model that exploits rotation and reflection symmetries. We demonstrate significant gains in sample efficiency due to increased weight sharing, as…
Rotation Equivariant CNNs for Digital Pathology
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens +2
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing rec…
Extraction of Airways using Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling +3
We present extraction of tree structures, such as airways, from image data as a graph refinement task. To this end, we propose a graph auto-encoder model that uses an encoder based…
Mean Field Network based Graph Refinement with application to Airway Tree Extraction
Raghavendra Selvan, Max Welling, Jesper H. Pedersen +2
We present tree extraction in 3D images as a graph refinement task, of obtaining a subgraph from an over-complete input graph. To this end, we formulate an approximate Bayesian inf…
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel +1
This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highl…
A New Method to Visualize Deep Neural Networks
Luisa M. Zintgraf, Taco S. Cohen, Max Welling
We present a method for visualising the response of a deep neural network to a specific input. For image data for instance our method will highlight areas that provide evidence in…