Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the
training, validation, and testing of advanced deep learning (DL)-based automated tools …

As medical imaging enters its information era and presents rapidly increasing needs for big
data analytics, robust pooling and harmonization of imaging data across diverse cohorts …

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling
non-biological variance introduced by differences in MRI scanners and acquisition …

Acquiring resting‐state functional magnetic resonance imaging (fMRI) datasets at multiple
MRI scanners and clinical sites can improve statistical power and generalizability of results …

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks
reproducibility between protocols and scanners. It has been shown that even when care is …

While aggregation of neuroimaging datasets from multiple sites and scanners can yield
increased statistical power, it also presents challenges due to systematic scanner effects …

Associations between datasets can be discovered through multivariate methods like
Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property …

Modern neuroimaging studies frequently combine data collected from multiple scanners and
experimental conditions. Such data often contain substantial technical variability associated …

Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient
information directly from images that are decoded into handcrafted features, comprising …