Searched for: subject%3A%22Pediatric%255C+cancer%22
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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)
Machine learning is impacting modern society at large, thanks to its increasing potential to effciently and effectively model complex and heterogeneous phenomena. While machine learning models can achieve very accurate predictions in many applications, they are not infallible. In some cases, machine learning models can deliver unreasonable...
doctoral thesis 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