4 citations · 7 across the 6 of their papers we have counts for
7 papers
Data variation-aware medical image segmentation
Arkadiy Dushatskiy, Gerry Lowe, Peter A. N. Bosman +1
Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowl…
Mixed-Block Neural Architecture Search for Medical Image Segmentation
Martijn M. A. Bosma, Arkadiy Dushatskiy, Monika Grewal +2
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cas…
Heed the Noise in Performance Evaluations in Neural Architecture Search
Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
Neural Architecture Search (NAS) has recently become a topic of great interest. However, there is a potentially impactful issue within NAS that remains largely unrecognized: noise.…
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter +2
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencie…
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning
Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness va…
Local Search is a Remarkably Strong Baseline for Neural Architecture Search
T. Den Ottelander, A. Dushatskiy, M. Virgolin +1
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being propos…