Zobrazeno 1 - 10
of 393
pro vyhledávání: '"Bosman, Peter"'
Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for medical image synthesis must be manually adjusted when they are applied t
Externí odkaz:
http://arxiv.org/abs/2404.06240
Traditional approaches to neuroevolution often start from scratch. This becomes prohibitively expensive in terms of computational and data requirements when targeting modern, deep neural networks. Using a warm start could be highly advantageous, e.g.
Externí odkaz:
http://arxiv.org/abs/2403.14224
Deformable image registration (DIR) involves optimization of multiple conflicting objectives, however, not many existing DIR algorithms are multi-objective (MO). Further, while there has been progress in the design of deep learning algorithms for DIR
Externí odkaz:
http://arxiv.org/abs/2402.16658
In the health domain, decisions are often based on different data modalities. Thus, when creating prediction models, multimodal fusion approaches that can extract and combine relevant features from different data modalities, can be highly beneficial.
Externí odkaz:
http://arxiv.org/abs/2402.12183
Bayesian networks model relationships between random variables under uncertainty and can be used to predict the likelihood of events and outcomes while incorporating observed evidence. From an eXplainable AI (XAI) perspective, such models are interes
Externí odkaz:
http://arxiv.org/abs/2402.12175
For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed very efficiently. It has been shown that such partial evaluations can
Externí odkaz:
http://arxiv.org/abs/2402.10757
Deploying machine learning models into sensitive domains in our society requires these models to be explainable. Genetic Programming (GP) can offer a way to evolve inherently interpretable expressions. GP-GOMEA is a form of GP that has been found par
Externí odkaz:
http://arxiv.org/abs/2402.09854
Autor:
Andreadis, Georgios, Mulder, Joas I., Bouter, Anton, Bosman, Peter A. N., Alderliesten, Tanja
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 typ
Externí odkaz:
http://arxiv.org/abs/2401.16867
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 p
Externí odkaz:
http://arxiv.org/abs/2307.15621
Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems involve two or more conflicting objectives. In this work, we therefor
Externí odkaz:
http://arxiv.org/abs/2306.01436