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OpenBHB: a Large-Scale Multi-Site Brain MRI Data-set for Age Prediction and Debiasing by Benoit Dufumier; Antoine Grigis; Julie Victor; Corentin Ambroise; Vincent Frouin; Edouard Duchesnay is a Neuroscience article available to read on EtoBox.

What is OpenBHB: a Large-Scale Multi-Site Brain MRI Data-set for Age Prediction and Debiasing about?

Prediction of chronological age from neuroimaging in the healthy population is an important issue because the deviations from normal brain age may highlight abnormal trajectories towards brain disorders. As a first step, ML models have emerged to predict chronological age from brain MRI, as a proxy measure of biological age. However, there is currently no consensus w.r.t which Machine Learning (ML) model is best suited for this task, largely because of a lack of public benchmark. Furthermore, new large emerging population neuroimaging datasets are often biased by the acquisition center images are coming from. This bias heavily deteriorates models generalization capacities, especially for Deep Learning (DL) algorithms that are known to overfit rapidly on the simplest features (known as simplicity bias). Here we propose a new public benchmarking resource, namely Open Big Healthy Brains (OpenBHB), along with a challenge for both brain age prediction and site-effect removal through a representation learning framework. OpenBHB is large-scale, gathering > 5K 3D T1 brain MRI from Healthy Controls (HC) and highly multi-sites, aggregating > 60 centers worldwide and 10 studies. OpenBHB is ex

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Author

Benoit Dufumier; Antoine Grigis; Julie Victor; Corentin Ambroise; Vincent Frouin; Edouard Duchesnay

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Neuroscience (Life Sciences)