[www] [BibTex]

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.

[www]

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.

[www] [BibTex]

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.

[www]

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.

[www]

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.

[www] [BibTex]

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.