2 citations · 2 across the 2 of their papers we have counts for
5 papers
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…
A Framework for Challenge Design: Insight and Deployment Challenges to Address Medical Image Analysis Problems
Adriënne M. Mendrik, Stephen R. Aylward
In this paper we aim to refine the concept of grand challenges in medical image analysis, based on statistical principles from quantitative and qualitative experimental research. W…
Beyond the Leaderboard: Insight and Deployment Challenges to Address Research Problems
Adrienne M. Mendrik, Stephen R. Aylward
In the medical image analysis field, organizing challenges with associated workshops at international conferences began in 2007 and has grown to include over 150 challenges. Severa…
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…