Breaking medical data sharing boundaries by using synthesized radiographs

Han T, Nebelung S, Haarburger C, Horse N, Reinartz S, Merhof D, Kiessling F, Schulzl VKR, Truhn D (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Science Advances American Association for the Advancement of Science

Book Volume: 6

Article Number: eabb7973

Journal Issue: 49

DOI: 10.1126/sciadv.abb7973

Abstract

Computer vision (CV) has the potential to change medicine fundamentally. Expert knowledge provided by CV can enhance diagnosis. Unfortunately, existing algorithms often remain below expectations, as databases used for training are usually too small, incomplete, and heterogeneous in quality. Moreover, data protection is a serious obstacle to the exchange of data. To overcome this limitation, we propose to use generative models (GMs) to produce high-resolution synthetic radiographs that do not contain any personal identification information. Blinded analyses by CV and radiology experts confirmed the high similarity of synthesized and real radiographs. The combination of pooled GM improves the performance of CV algorithms trained on smaller datasets, and the integration of synthesized data into patient data repositories can compensate for underrepresented disease entities. By integrating federated learning strategies, even hospitals with few datasets can contribute to and benefit from GM training.

Involved external institutions

Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) ARISTRA GmbH DE Germany (DE) Universitätsklinikum Düsseldorf DE Germany (DE) Fraunhofer-Institut für Bildgestützte Medizin (MEVIS) DE Germany (DE)

How to cite

APA:

Han, T., Nebelung, S., Haarburger, C., Horse, N., Reinartz, S., Merhof, D.,... Truhn, D. (2020). Breaking medical data sharing boundaries by using synthesized radiographs. Science Advances, 6(49). https://doi.org/10.1126/sciadv.abb7973

MLA:

Han, Tianyu, et al. "Breaking medical data sharing boundaries by using synthesized radiographs." Science Advances 6.49 (2020).

BibTeX: Download