Searched for: collection%253Air
(1 - 20 of 23)

Pages

List view
Grid view
document
Dickhoff, Leah R.M. (author), Scholman, R.J. (author), Barten, Danique L.J. (author), Kerkhof, Ellen M. (author), Roorda, Jelmen J. (author), Velema, Laura A. (author), Stalpers, Lukas J.A. (author), Pieters, Bradley R. (author), Bosman, P.A.N. (author), Alderliesten, T. (author)
PURPOSE: Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single “optimal” plan, finding multiple, yet different near-optimal plans, can be an insightful approach to support radiation oncologists in finding the plan they are looking for. METHODS AND...
review 2024
document
Andreadis, Georgios (author), Mulder, Joas I. (author), Bouter, P.A. (author), Bosman, P.A.N. (author), Alderliesten, T. (author)
The transformation model is an essential component of any deformable image registration approach. It provides a representation of physical deformations between images, thereby defining the range and realism of registrations that can be found. Two types of transformation models have emerged as popular choices: B-spline models and mesh models....
conference paper 2024
document
Grewal, M. (author), Wiersma, Jan (author), Westerveld, Henrike (author), Bosman, P.A.N. (author), Alderliesten, T. (author)
Purpose: Deformable image registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a deep convolutional neural...
journal article 2023
document
Guijt, Arthur (author), Thierens, Dirk (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the algorithm and wastes computational resources. Alternatively, an EA can be made asynchronous parallel. However, EAs using classic recombination and selection operators...
conference paper 2023
document
Harrison, Joe (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at...
conference paper 2023
document
Chebykin, Alexander (author), Dushatskiy, A. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
In this work, we show that simultaneously training and mixing neural networks is a promising way to conduct Neural Architecture Search (NAS). For hyperparameter optimization, reusing the partially trained weights allows for efficient search, as was previously demonstrated by the Population Based Training (PBT) algorithm. We propose PBT-NAS,...
conference paper 2023
document
Deist, Timo M. (author), Grewal, M. (author), Dankers, Frank J.W.M. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Real-world problems are often multi-objective, with decision-makers unable to specify a priori which trade-off between the conflicting objectives is preferable. Intuitively, building machine learning solutions in such cases would entail providing multiple predictions that span and uniformly cover the Pareto front of all optimal trade-off...
conference paper 2023
document
van Bindsbergen, Kelly L.A. (author), van der Hoek, Hinke (author), van Gorp, Marloes (author), Ligthart, Mike E.U. (author), Hindriks, K.V. (author), Neerincx, M.A. (author), Alderliesten, T. (author), Bosman, P.A.N. (author), Merks, Johannes H.M. (author)
Objectives: Children with cancer often experience sleep problems, which are associated with many negative physical and psychological health outcomes, as well as with a lower quality of life. Therefore, interventions are strongly required to improve sleep in this population. We evaluated interactive education with respect to sleep hygiene with...
journal article 2022
document
Dushatskiy, A. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
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. Due to stochastic factors in neural network initialization, training, and the chosen train/validation dataset split, the performance evaluation of a neural network...
conference paper 2022
document
Dushatskiy, A. (author), Lowe, Gerry (author), Bosman, P.A.N. (author), Alderliesten, T. (author)
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 acknowledged to still be a problem. One way to automatically overcome this is to capture and exploit this variation explicitly. Here, we propose an approach that improves...
conference paper 2022
document
Bosma, Martijn M.A. (author), Dushatskiy, A. (author), Grewal, M. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
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 cases human-level, performance, they have become the standard approach in this field. The design of the best possible medical image segmentation DNNs, however, is task...
conference paper 2022
document
Chebykin, Alexander (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery of task-specific network architectures. Modern NAS approaches leverage super-networks whose subnetworks encode candidate neural network architectures. These...
conference paper 2022
document
Liu, D. (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sorting genetic algorithm II (NSGA-II) is widely used. Unfortunately, it has been shown that NSGA-II can be inefficient: in early generations, low-complexity models over...
conference paper 2022
document
Guijt, Arthur (author), Thierens, Dirk (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue of linkage (or variable interaction) learning. This requires, however, that the linkage model can capture the exploitable structure of a problem. Usually, a single type of linkage structure is attempted to be captured using models such as a linkage tree. However, in...
conference paper 2022
document
Harrison, J. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Genetic Programming (GP) can make an important contribution to explainable artificial intelligence because it can create symbolic expressions as machine learning models. Nevertheless, to be explainable, the expressions must not become too large. This may, however, limit their potential to be accurate. The re-use of subexpressions has the...
conference paper 2022
document
Scholman, R.J. (author), Bouter, Anton (author), Dickhoff, Leah R.M. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found set of solutions is not smoothly navigable because the solutions belong to various niches,...
conference paper 2022
document
Dushatskiy, A. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Pool Optimal Mixing Algorithm (GOMEA) and adapt the resulting algorithm for solving non-binary...
conference paper 2021
document
Virgolin, M. (author), Wang, Ziyuan (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR)...
journal article 2020
document
Virgolin, M. (author), Wang, Ziyuan (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
The advent of Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To capture the effects of radiation treatment, treatment plans are typically simulated...
conference paper 2020
document
Bouter, P.A. (author), Maree, S.C. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Often, real-world problems are of the gray-box type. It has been shown that the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) is in principle capable of exploiting such a Gray-Box Optimization (GBO) setting using linkage models that capture dependencies between problem variables, resulting in excellent performance and...
conference paper 2020
Searched for: collection%253Air
(1 - 20 of 23)

Pages

List view
Grid view