Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets

Marc-Andre Schulz, B.T. Thomas Yeo, Joshua T. Vogelstein, Janaina Mourao-Miranada, Jakob N. Kather, Konrad Kording, View ORCID ProfileBlake Richards, Danilo Bzdok
doi: https://doi.org/10.1101/757054
Marc-Andre Schulz
1Department of Psychiatry, Psychotherapy, and Psychosomatics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
B.T. Thomas Yeo
2Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore
3Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
4Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joshua T. Vogelstein
5Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, USA
6Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Janaina Mourao-Miranada
7Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, UK
8Centre for Medical Image Computing, Department of Computer Science, University College London, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jakob N. Kather
9Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
10German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
11Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Konrad Kording
12Department of Neuroscience and Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Blake Richards
13Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
14School of Computer Science, McGill University, Montréal, Québec, Canada
15Canadian Institute for Advanced Research, Toronto, Ontario, Canada
16Mila, Montréal, Québec, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Blake Richards
Danilo Bzdok
1Department of Psychiatry, Psychotherapy, and Psychosomatics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
17Neurospin, Commissariat à l’Energie Atomique (CEA) Saclay, Gif-sur-Yvette, France
18Parietal Team, Institut National de Recherche en Informatique et en Automatique (INRIA), France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: danilo.bzdok@rwth-aachen.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

In recent years, deep learning has unlocked unprecedented success in various domains, especially in image, text, and speech processing. These breakthroughs may hold promise for neuroscience and especially for brain-imaging investigators who start to analyze thousands of participants. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at currently available sample sizes. We systematically profiled the performance of deep models, kernel models, and linear models as a function of sample size on UK Biobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improved when escalating from linear models to shallow-nonlinear models, and further improved when switching to deep-nonlinear models. The more observations were available for model training, the greater the performance gain we saw. In contrast, using structural or functional brain scans, simple linear models performed on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In fact, linear models kept improving as the sample size approached ∼10,000 participants. Our results indicate that the increase in performance of linear models with additional data does not saturate at the limit of current feasibility. Yet, nonlinearities of common brain scans remain largely inaccessible to both kernel and deep learning methods at any examined scale.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted September 06, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets
Marc-Andre Schulz, B.T. Thomas Yeo, Joshua T. Vogelstein, Janaina Mourao-Miranada, Jakob N. Kather, Konrad Kording, Blake Richards, Danilo Bzdok
bioRxiv 757054; doi: https://doi.org/10.1101/757054
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets
Marc-Andre Schulz, B.T. Thomas Yeo, Joshua T. Vogelstein, Janaina Mourao-Miranada, Jakob N. Kather, Konrad Kording, Blake Richards, Danilo Bzdok
bioRxiv 757054; doi: https://doi.org/10.1101/757054

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (2513)
  • Biochemistry (4957)
  • Bioengineering (3456)
  • Bioinformatics (15148)
  • Biophysics (6868)
  • Cancer Biology (5365)
  • Cell Biology (7692)
  • Clinical Trials (138)
  • Developmental Biology (4509)
  • Ecology (7117)
  • Epidemiology (2059)
  • Evolutionary Biology (10193)
  • Genetics (7494)
  • Genomics (9758)
  • Immunology (4808)
  • Microbiology (13153)
  • Molecular Biology (5114)
  • Neuroscience (29321)
  • Paleontology (203)
  • Pathology (833)
  • Pharmacology and Toxicology (1458)
  • Physiology (2123)
  • Plant Biology (4723)
  • Scientific Communication and Education (1004)
  • Synthetic Biology (1336)
  • Systems Biology (3997)
  • Zoology (768)