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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
verfasst von : Ilja Manakov, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten Kortuem, Volker Tresp
Erschienen in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data
Abstract
We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between these domains on unpaired retinal optical coherence tomography images. In quantitative measurements and a qualitative evaluation by ophthalmologists, we show how this approach outperforms other established methods. The results indicate that the network differentiates subtle changes in the level of noise in the image. Further investigation of the model’s feature maps reveals that it has learned to distinguish retinal layers and other distinct regions of the images.