Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Marc-Andre Schulz, B. T. Thomas Yeo, Joshua T. Vogelstein, Janaina Mourao-Miranada, Jakob N. Kather, Konrad Kording, Blake Richards and Danilo Bzdok ()
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Marc-Andre Schulz: Aachen University
B. T. Thomas Yeo: National University of Singapore
Joshua T. Vogelstein: Johns Hopkins University
Janaina Mourao-Miranada: University College London
Jakob N. Kather: University Hospital RWTH Aachen
Konrad Kording: University of Pennsylvania
Blake Richards: McGill University
Danilo Bzdok: Mila - Quebec Artificial Intelligence Institute

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models. In contrast, using structural or functional brain scans, simple linear models perform on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In sum, linear models keep improving as the sample size approaches ~10,000 subjects. Yet, nonlinearities for predicting common phenotypes from typical brain scans remain largely inaccessible to the examined kernel and deep learning methods.

Date: 2020
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DOI: 10.1038/s41467-020-18037-z

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