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REFACING: RECONSTRU...
REFACING: RECONSTRUCTING ANONYMIZED FACIAL FEATURES USING GANS
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- Abramian, David, 1992- (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
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- Eklund, Anders (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Statistik och maskininlärning,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV
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(creator_code:org_t)
- IEEE, 2019
- 2019
- Engelska.
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Ingår i: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019). - : IEEE. - 9781538636411 ; , s. 1104-1108
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in deep learning may raise the bar on the amount of distortion that needs to be applied to guarantee anonymity. To test such possibilities, we have applied the novel CycleGAN unsupervised image-to-image translation framework on sagittal slices of T1 MR images, in order to reconstruct, facial features from anonymized data. We applied the CycleGAN framework on both face-blurred and face-removed images. Our results show that face blurring may not provide adequate protection against malicious attempts at identifying the subjects, while face removal provides more robust anonymization, but is still partially reversible.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- MRI; anonymization; GANs; image-to-image translation
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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