An automated machine learning approach to predict brain age from cortical anatomical measures
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Dafflon:2020:HBM,
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author = "Jessica Dafflon and Walter H. L. Pinaya and
Federico Turkheimer and James H. Cole and Robert Leech and
Mathew A. Harris and Simon R. Cox and
Heather C. Whalley and Andrew M. McIntosh and Peter J. Hellyer",
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title = "An automated machine learning approach to predict
brain age from cortical anatomical measures",
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journal = "Human Brain Mapping",
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year = "2020",
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volume = "41",
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number = "13",
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pages = "3555--3566",
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keywords = "genetic algorithms, genetic programming, TPOT, age
prediction, automated machine learning, cortical
features, neuroimaging, predictive modeling, structural
imaging",
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ISSN = "1065-9471",
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URL = "https://arxiv.org/abs/1910.03349",
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URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25028",
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DOI = "doi:10.1002/hbm.25028",
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size = "12 pages",
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abstract = "The use of machine learning (ML) algorithms has
significantly increased in neuroscience. However, from
the vast extent of possible ML algorithms, which one is
the optimal model to predict the target variable? What
are the hyperparameters for such a model? Given the
plethora of possible answers to these questions, in the
last years, automated ML (autoML) has been gaining
attention. Here, we apply an autoML library called
Tree‐based Pipeline Optimisation Tool (TPOT) which
uses a tree‐based representation of ML pipelines and
conducts a genetic programming based approach to find
the model and its hyperparameters that more closely
predicts the subject's true age. To explore autoML and
evaluate its efficacy within neuroimaging data sets, we
chose a problem that has been the focus of previous
extensive study: brain age prediction. Without any
prior knowledge, TPOT was able to scan through the
model space and create pipelines that outperformed the
state‐of‐the‐art accuracy for Freesurfer‐based
models using only thickness and volume information for
anatomical structure. In particular, we compared the
performance of TPOT (mean absolute error [MAE]:
4.612 plus/minus .124 years) and a relevance
vector regression (MAE
5.474 plus/minus .140 years). TPOT also suggested
interesting combinations of models that do not match
the current most used models for brain prediction but
generalise well to unseen data. AutoML showed promising
results as a data‐driven approach to find optimal
models for neuroimaging applications.",
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notes = "14000 GP entry.
PMID: 32415917
Age UK‐funded",
- }
Genetic Programming entries for
Jessica De Faria Dafflon
Walter Hugo Lopez Pinaya
Federico Turkheimer
James H Cole
Robert Leech
Mathew A Harris
Simon R Cox
Heather C Whalley
Andrew M McIntosh
Peter J Hellyer
Citations