DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
Yang, C.-H. Huck
KAUST DepartmentKing Abdullah University of Science and Technology (KAUST)
Biological and Environmental Sciences and Engineering (BESE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/665874
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AbstractIn 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.
CitationHuang, J.-H., Huck Yang, C.-H., Liu, F., Tian, M., Liu, Y.-C., Wu, T.-W., … Worring, M. (2021). DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv48630.2021.00249
SponsorsThis work is supported by competitive research funding from King Abdullah University of Science and Technology (KAUST) and University of Amsterdam.
Conference/Event name2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
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Title: Jhhuangkay/DeepOpht-Medical-Report-Generation-for-Retinal-Images-via-Deep-Models-and-Visual-Explanation:. Publication Date: 2020-10-31. github: Jhhuangkay/DeepOpht-Medical-Report-Generation-for-Retinal-Images-via-Deep-Models-and-Visual-Explanation Handle: 10754/670118