DeepSynthBody: The beginning of the end for data deficiency in medicine

Vajira Thambawita*, Steven A. Hicks, Jonas Isaksen, Mette Haug Stensen, Trine B. Haugen, Jorgen Kanters, Sravanthi Parasa, Thomas De Lange, Havard D. Johansen, Dag Johansen, Hugo L. Hammer, Pal Halvorsen, Michael A. Riegler

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

12 Citations (Scopus)

Abstract

Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.

Original languageEnglish
Title of host publication2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021
PublisherIEEE
Publication date2021
Pages1-8
ISBN (Electronic)978-1-7281-5934-8
DOIs
Publication statusPublished - 2021
Event2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021 - Halden, Norway
Duration: 19 May 202121 May 2021

Conference

Conference2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021
Country/TerritoryNorway
CityHalden
Period19/05/202121/05/2021
SponsorInstitute for Energy Technology, Ostfold University College

Keywords

  • deep synthetic human body
  • DeepSynth augmentation
  • DeepSynth explainable AI
  • DeepSynthBody
  • explainable DeepSynth
  • GAN
  • medical data privacy
  • multi-model DeepSynth
  • privacy issue
  • synthetic data
  • synthetic medical data

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