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Machine Learning Algorithms for Radiogenomics: application to Prediction of the MGMT promoter methylation status in mpMRI scans

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KaramiMostafa/ML-for-Radiogenomics

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Data

The data is defined by three cohorts: Training, Validation (Public), and Testing (Private). The “Training” and the “Validation” cohorts are provided to the participants, whereas the “Testing” cohort is kept hidden at all times, during and after the competition. These 3 cohorts are structured as follows: Each independent case has a dedicated folder identified by a five-digit number. Within each of these “case” folders, there are four sub-folders, each of them corresponding to each of the structural multi-parametric MRI (mpMRI) scans, in DICOM format. The exact mpMRI scans included are:

Fluid Attenuated Inversion Recovery (FLAIR)
T1-weighted pre-contrast (T1w)
T1-weighted post-contrast (T1Gd)
T2-weighted (T2)

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Machine Learning Algorithms for Radiogenomics: application to Prediction of the MGMT promoter methylation status in mpMRI scans

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