Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks

By Jim Winkens, Jasper Linmans, Bastiaan S. Veeling, Taco Cohen, Max Welling. 2018

17 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Maurice Weiler
2. Taco Cohen
3. David W. Romero
4. Mario Geiger
5. Erik J. Bekkers
6. Max Welling
7. Daniel E. Worrall
8. Berkay Kicanaoglu

Citations in the corpus

• Gauge Equivariant Convolutional Networks and the Icosahedral CNN (2019, background): “…rotations of the plane and 3D space (Oyallon & Mallat, 2015; Cohen & Welling, 2016; 2017; Marcos et al., 2017; Weiler et al., 2018b;a; Worrall et al., 2017; Worrall & Brostow, 2018; Winkels & Cohen, 2018; Winkens et al., 2018; Thomas et al., 2018; Bekkers et al., 2018), and the sphere (see below).”
• A General Theory of Equivariant CNNs on Homogeneous Spaces (2018, methodology): “For segmentation tasks, one can use a trivial representation for ρ2 in the output layer to obtain a scalar feature map on S(2), analogous to [43].”