1. Training deep learning models to work on multiple devices by cross domain learning with no additional annotations

    Training deep learning models to work on multiple devices by cross domain learning with no additional annotations

    Purpose To create an unsupervised cross domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic macular optical coherence tomography (OCT) images from different manufacturers and camera devices. Design We sought to use Generative Adversarial Networks (GAN) to generalize a segmentation model trained on one OCT device to segment B-scans obtained from a different OCT device manufacturer in a fully unsupervised approach without labeled data from the latter manufacturer. Subjects A total of 732 OCT B-scans from four different OCT devices (Heidelberg Spectralis, Topcon 1000, Maestro2, and Zeiss Plex Elite 9000). Methods We developed an unsupervised ...

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