1. Clustering Spectral-Domain Optical Coherence Tomography Images using a Deep Variational Auto-encoder

    Clustering Spectral-Domain Optical Coherence Tomography Images using a Deep Variational Auto-encoder

    Purpose : To learn a low-dimensional representation of spectral-domain optical coherence tomography (SD OCT) peripapillary images that can be used to classify images into glaucoma versus healthy eyes. Methods : The study included 23,992 Spectralis SD OCT images from 1,336 eyes, of which 30% were healthy and the remaining were glaucoma or glaucoma suspects. The definition of groups was based on visual fields and inspection of the optic nerve. In order to learn a low-dimensional representation of the high-dimensional SD OCT images a variational auto-encoder (VAE) was used, an unsupervised deep learning technique. The encoder and decoder of the VAE ...

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