Journal Publications
| [191962] |
| Title: TrainingPhantoms.jl: Simple and Versatile Image Phantom Generation. |
| Written by: P. Jürß, C. Droigk, M. Boberg, and T. Knopp |
| in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025). |
| Volume: <strong>11</strong>. Number: (1 Suppl 1), |
| on pages: 1-2 |
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| DOI: https://doi.org/10.18416/IJMPI.2025.2503031 |
| URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/802 |
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Note: inproceedings, opensoftware, generalsoftware
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.
Conference Proceedings
| [191962] |
| Title: TrainingPhantoms.jl: Simple and Versatile Image Phantom Generation. |
| Written by: P. Jürß, C. Droigk, M. Boberg, and T. Knopp |
| in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025). |
| Volume: <strong>11</strong>. Number: (1 Suppl 1), |
| on pages: 1-2 |
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| DOI: https://doi.org/10.18416/IJMPI.2025.2503031 |
| URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/802 |
| ARXIVID: |
| PMID: |
Note: inproceedings, opensoftware, generalsoftware
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.
