DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification
Prezja, F., Paloneva, J., Pölönen, I., Niinimäki, E., & Äyrämö, S. (2022). DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification. Scientific Reports, 12, Article 18573. https://doi.org/10.1038/s41598-022-23081-4
Julkaistu sarjassa
Scientific ReportsPäivämäärä
2022Oppiaine
TietotekniikkaComputing, Information Technology and MathematicsHuman and Machine based Intelligence in LearningLaskennallinen tiedeMathematical Information TechnologyComputing, Information Technology and MathematicsHuman and Machine based Intelligence in LearningComputational ScienceTekijänoikeudet
© The Author(s) 2022
Recent developments in deep learning have impacted medical science. However, new privacy issues and regulatory frameworks have hindered medical data sharing and collection. Deep learning is a very data-intensive process for which such regulatory limitations limit the potential for new breakthroughs and collaborations. However, generating medically accurate synthetic data can alleviate privacy issues and potentially augment deep learning pipelines. This study presents generative adversarial neural networks capable of generating realistic images of knee joint X-rays with varying osteoarthritis severity. We offer 320,000 synthetic (DeepFake) X-ray images from training with 5,556 real images. We validated our models regarding medical accuracy with 15 medical experts and for augmentation effects with an osteoarthritis severity classification task. We devised a survey of 30 real and 30 DeepFake images for medical experts. The result showed that on average, more DeepFakes were mistaken for real than the reverse. The result signified sufficient DeepFake realism for deceiving the medical experts. Finally, our DeepFakes improved classification accuracy in an osteoarthritis severity classification task with scarce real data and transfer learning. In addition, in the same classification task, we replaced all real training data with DeepFakes and suffered only a 3.79% loss from baseline accuracy in classifying real osteoarthritis X-rays.
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Julkaisija
Nature Publishing GroupISSN Hae Julkaisufoorumista
2045-2322Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/160107769
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Lisätietoja rahoituksesta
The work is related to the AI Hub Central Finland project that has received funding from Council of Tampere Region and European Regional Development Fund and Leverage from the EU 2014–2020. This project has been funded with support from the European Commission. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein. ...Lisenssi
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