Unsupervised Deep Learning Method for Bias Correction
Abstract
In this paper, a new method for automatic MR image inhomogeneity correction is proposed. This method, based on deep learning, uses unsupervised learning to estimate the bias corrected images minimizing a cost function based on the entropy of the corrupted image, the derivative of the estimated bias field and corrected image statistics. The proposed method has been compared with the state-of-the-art method N4 providing improved results.
Cite
Text
Perez-Caballero et al. "Unsupervised Deep Learning Method for Bias Correction." Proceedings of MIDL 2024, 2024.Markdown
[Perez-Caballero et al. "Unsupervised Deep Learning Method for Bias Correction." Proceedings of MIDL 2024, 2024.](https://mlanthology.org/midl/2024/perezcaballero2024midl-unsupervised/)BibTeX
@inproceedings{perezcaballero2024midl-unsupervised,
title = {{Unsupervised Deep Learning Method for Bias Correction}},
author = {Perez-Caballero, Maria and Morell-Ortega, Sergio and Perez, Marina Ruiz and Coupe, Pierrick and Manjon, Jose V},
booktitle = {Proceedings of MIDL 2024},
year = {2024},
pages = {1098-1106},
volume = {250},
url = {https://mlanthology.org/midl/2024/perezcaballero2024midl-unsupervised/}
}