9 citations · 13 across the 5 of their papers we have counts for
5 papers · 1 filter
A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging
Nairouz Shehata, Wulfie Bain, Ben Glocker
Graph neural networks have emerged as a promising approach for the analysis of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays an important role for mod…
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
Maxime W. Lafarge, Erik J. Bekkers, Josien P. W. Pluim +2
Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular for computational pathology applications. We propose a framework t…
Attentive Group Equivariant Convolutional Networks
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak +1
Although group convolutional networks are able to learn powerful representations based on symmetry patterns, they lack explicit means to learn meaningful relationships among them (…
Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
Erik J Bekkers, Maxime W Lafarge, Mitko Veta +3
We propose a framework for rotation and translation covariant deep learning using group convolutions. The group product of the special Euclidean motion group descri…
Design and Processing of Invertible Orientation Scores of 3D Images for Enhancement of Complex Vasculature
M. H. J. Janssen, A. J. E. M. Janssen, E. J. Bekkers +2
The enhancement and detection of elongated structures in noisy image data is relevant for many biomedical imaging applications. To handle complex crossing structures in 2D images,…