Noise transfer for unsupervised domain adaptation of retinal OCT images. (arXiv:2209.08097v1 [cs.CV])

Optical coherence tomography (OCT) imaging from different camera devices
causes challenging domain shifts and can cause a severe drop in accuracy for
machine learning models. In this work, we introduce a minimal noise adaptation
method based on a singular value decomposition (SVDNA) to overcome the domain
gap between target domains from three different device manufacturers in retinal
OCT imaging. Our method utilizes the difference in noise structure to
successfully bridge the domain gap between different OCT devices and transfer
the …

arxiv domain adaptation images noise transfer unsupervised