123 citations · 282 across the 44 of their papers we have counts for
4 papers · 1 filter
Convolutions, Transformers, and their Ensembles for the Segmentation of Organs at Risk in Radiation Treatment of Cervical Cancer
Vangelis Kostoulas, Peter A. N. Bosman, Tanja Alderliesten
Segmentation of regions of interest in images of patients, is a crucial step in many medical procedures. Deep neural networks have proven to be particularly adept at this task. How…
Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations
Monika Grewal, Dustin van Weersel, Henrike Westerveld +2
Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the aut…
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…