2 citations · 2 across the 2 of their papers we have counts for
3 papers · 1 filter
The Data Representativeness Criterion: Predicting the Performance of Supervised Classification Based on Data Set Similarity
Evelien Schat, Rens van de Schoot, Wouter M. Kouw +2
In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus…
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
Arkadiy Dushatskiy, Adriënne M. Mendrik, Peter A. N. Bosman +1
There has recently been great progress in automatic segmentation of medical images with deep learning algorithms. In most works observer variation is acknowledged to be a problem a…
Learning an MR acquisition-invariant representation using Siamese neural networks
Wouter M. Kouw, Marco Loog, Wilbert Bartels +1
Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we pr…