@article{JürßTrainingPhantoms2024IWMPI,
Author = {P. Jürß, C. Droigk, M. Boberg, and T. Knopp},
Title = {TrainingPhantoms.jl: Simple and Versatile Image Phantom Generation.},
Journal = {<em>International Journal on Magnetic Particle Imaging</em>.},
Year = {(2025).},
Volume = {<strong>11</strong>.},
Number = {(1 Suppl 1),},
Pages = {1-2},
Month = {Mar},
Note = {inproceedings, opensoftware, generalsoftware},
Doi = {10.18416/IJMPI.2025.2503031},
Url = {https://www.journal.iwmpi.org/index.php/iwmpi/article/view/802},
Keywords = {inproceedings},
Abstract = {Large collections of labeled data play a crucial role in supervised machine learning projects. Unfortunately, such datasets are quite rare in the medical domain. In this work, the Julia project TrainingPhantoms.jl is introduced, which provides a simple interface to generate large and diverse collections of randomly generated image phantoms. The proposed phantom generator has been successfully used to train an image quality enhancement network that managed to generalize to unseen experimental out-of-distribution data.}
}

@COMMENT{Bibtex file generated on 2026-5-13 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/ibi/people/marija-boberg }