In this section
@inbook{Jimenez-Pastor:2025:10.1007/978-3-031-89963-8_6,
author = {Jimenez-Pastor, A and Cerdá-Alberich, L and Kosvyra, A and Nan, Y and Xing, X and Li, R and Yang, G},
booktitle = {Trustworthy AI in Cancer Imaging Research},
doi = {10.1007/978-3-031-89963-8_6},
pages = {121--142},
title = {Data Harmonization and Challenges Toward the Generation of Repositories: Sharing Practices and Approaches},
url = {http://dx.doi.org/10.1007/978-3-031-89963-8_6},
year = {2025}
}
TY - CHAP
AB - The harmonization of data is becoming increasingly important in imaging research. As the complexity and volume of data grow, integrating diverse datasets from different institutions, regions, and imaging modalities presents significant challenges and opportunities. This chapter delves into the role that data harmonization plays in overcoming these challenges and facilitating the generation of comprehensive data repositories that can be shared across research groups and institutions, detailing the different steps required to build such repositories including data de-identification, image annotation, quality control, and harmonization.
AU - Jimenez-Pastor,A
AU - Cerdá-Alberich,L
AU - Kosvyra,A
AU - Nan,Y
AU - Xing,X
AU - Li,R
AU - Yang,G
DO - 10.1007/978-3-031-89963-8_6
EP - 142
PY - 2025///
SP - 121
TI - Data Harmonization and Challenges Toward the Generation of Repositories: Sharing Practices and Approaches
T1 - Trustworthy AI in Cancer Imaging Research
UR - http://dx.doi.org/10.1007/978-3-031-89963-8_6
ER -
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