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    DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation

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    Preprintfile1.pdf
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    Type
    Preprint
    Authors
    Huang, Jia-Hong
    Yang, Chao-Han Huck
    Liu, Fangyu
    Tian, Meng
    Liu, Yi-Chieh
    Wu, Ting-Wei
    Lin, I-Hung
    Wang, Kang
    Morikawa, Hiromasa
    Chang, Hernghua
    Tegner, Jesper cc
    Worring, Marcel
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Bioscience Program
    Date
    2020-11-01
    Permanent link to this record
    http://hdl.handle.net/10754/665874
    
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    Abstract
    In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologists to qualitatively show, the proposed AI-based method is effective. With our experimental results, we show that the proposed method is quantitatively and qualitatively effective. Our method is capable of creating meaningful retinal image descriptions and visual explanations that are clinically relevant.
    Sponsors
    This work is supported by competitive research funding from King Abdullah University of Science and Technology (KAUST) and University of Amsterdam.
    Publisher
    arXiv
    arXiv
    2011.00569
    Additional Links
    https://arxiv.org/pdf/2011.00569
    Collections
    Biological and Environmental Sciences and Engineering (BESE) Division; Preprints; Bioscience Program

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