1. Automatic Classification of Volumetric Optical Coherence Tomography Images via Recurrent Neural Network

    Automatic Classification of Volumetric Optical Coherence Tomography Images via Recurrent Neural Network

    Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologists in the diagnosis and grading of macular diseases. Most existing methods classify 3-D retinal OCT volumes by separately analyzing each single-frame 2-D B-scan, and thus inevitably ignore significant temporal information among B-scans. In this paper, we propose to classify volumetric OCT images via a recurrent neural network (VOCT-RNN) which can fully exploit temporal information among B-scans. Specifically, a deep convolutional neural network is first utilized to automatically extract highly representative features from each individual B-scan of the 3-D retinal OCT images. Then, a long ...

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