1. Attention-guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association using Volumetric Images

    Attention-guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association using Volumetric Images

    The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-art solution to accommodate for the limited number of training volumes as well as the available computing resources. However, this limits the network's ability to learn from small retinal structures in OCT volumes. In this paper, our goal is to improve the performance by providing guidance to DL model during training in order to learn from finer ocular structures in 3D OCT volumes. Therefore ...

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