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


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.

SUBMITTER: Schulz MA 

PROVIDER: S-EPMC7447816 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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

Schulz Marc-Andre MA   Yeo B T Thomas BTT   Vogelstein Joshua T JT   Mourao-Miranada Janaina J   Kather Jakob N JN   Kording Konrad K   Richards Blake B   Bzdok Danilo D  

Nature communications 20200825 1


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 consiste  ...[more]

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