RT Journal Article
SR Electronic
T1 Breaking medical data sharing boundaries by using synthesized radiographs
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP eabb7973
DO 10.1126/sciadv.abb7973
VO 6
IS 49
A1 Han, Tianyu
A1 Nebelung, Sven
A1 Haarburger, Christoph
A1 Horst, Nicolas
A1 Reinartz, Sebastian
A1 Merhof, Dorit
A1 Kiessling, Fabian
A1 Schulz, Volkmar
A1 Truhn, Daniel
YR 2020
UL http://advances.sciencemag.org/content/6/49/eabb7973.abstract
AB 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.