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

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

DeepSynthBody : The beginning of the end for data deficiency in medicine. / Thambawita, Vajira; Hicks, Steven A.; Isaksen, Jonas; Stensen, Mette Haug; Haugen, Trine B.; Kanters, Jorgen; Parasa, Sravanthi; De Lange, Thomas; Johansen, Havard D.; Johansen, Dag; Hammer, Hugo L.; Halvorsen, Pal; Riegler, Michael A.

2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021. IEEE, 2021. s. 1-8.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Thambawita, V, Hicks, SA, Isaksen, J, Stensen, MH, Haugen, TB, Kanters, J, Parasa, S, De Lange, T, Johansen, HD, Johansen, D, Hammer, HL, Halvorsen, P & Riegler, MA 2021, DeepSynthBody: The beginning of the end for data deficiency in medicine. i 2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021. IEEE, s. 1-8, 2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021, Halden, Norge, 19/05/2021. https://doi.org/10.1109/ICAPAI49758.2021.9462062

APA

Thambawita, V., Hicks, S. A., Isaksen, J., Stensen, M. H., Haugen, T. B., Kanters, J., Parasa, S., De Lange, T., Johansen, H. D., Johansen, D., Hammer, H. L., Halvorsen, P., & Riegler, M. A. (2021). DeepSynthBody: The beginning of the end for data deficiency in medicine. I 2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021 (s. 1-8). IEEE. https://doi.org/10.1109/ICAPAI49758.2021.9462062

Vancouver

Thambawita V, Hicks SA, Isaksen J, Stensen MH, Haugen TB, Kanters J o.a. DeepSynthBody: The beginning of the end for data deficiency in medicine. I 2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021. IEEE. 2021. s. 1-8 https://doi.org/10.1109/ICAPAI49758.2021.9462062

Author

Thambawita, Vajira ; Hicks, Steven A. ; Isaksen, Jonas ; Stensen, Mette Haug ; Haugen, Trine B. ; Kanters, Jorgen ; Parasa, Sravanthi ; De Lange, Thomas ; Johansen, Havard D. ; Johansen, Dag ; Hammer, Hugo L. ; Halvorsen, Pal ; Riegler, Michael A. / DeepSynthBody : The beginning of the end for data deficiency in medicine. 2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021. IEEE, 2021. s. 1-8

Bibtex

@inproceedings{3dd08caa139e486f892943e8cf3aa924,
title = "DeepSynthBody: The beginning of the end for data deficiency in medicine",
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.",
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",
author = "Vajira Thambawita and Hicks, {Steven A.} and Jonas Isaksen and Stensen, {Mette Haug} and Haugen, {Trine B.} and Jorgen Kanters and Sravanthi Parasa and {De Lange}, Thomas and Johansen, {Havard D.} and Dag Johansen and Hammer, {Hugo L.} and Pal Halvorsen and Riegler, {Michael A.}",
year = "2021",
doi = "10.1109/ICAPAI49758.2021.9462062",
language = "English",
pages = "1--8",
booktitle = "2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021",
publisher = "IEEE",
note = "2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021 ; Conference date: 19-05-2021 Through 21-05-2021",

}

RIS

TY - GEN

T1 - DeepSynthBody

T2 - 2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021

AU - Thambawita, Vajira

AU - Hicks, Steven A.

AU - Isaksen, Jonas

AU - Stensen, Mette Haug

AU - Haugen, Trine B.

AU - Kanters, Jorgen

AU - Parasa, Sravanthi

AU - De Lange, Thomas

AU - Johansen, Havard D.

AU - Johansen, Dag

AU - Hammer, Hugo L.

AU - Halvorsen, Pal

AU - Riegler, Michael A.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - deep synthetic human body

KW - DeepSynth augmentation

KW - DeepSynth explainable AI

KW - DeepSynthBody

KW - explainable DeepSynth

KW - GAN

KW - medical data privacy

KW - multi-model DeepSynth

KW - privacy issue

KW - synthetic data

KW - synthetic medical data

UR - http://www.scopus.com/inward/record.url?scp=85113768320&partnerID=8YFLogxK

U2 - 10.1109/ICAPAI49758.2021.9462062

DO - 10.1109/ICAPAI49758.2021.9462062

M3 - Article in proceedings

AN - SCOPUS:85113768320

SP - 1

EP - 8

BT - 2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021

PB - IEEE

Y2 - 19 May 2021 through 21 May 2021

ER -

ID: 298378496