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dc.contributor.authorHuang, Jia-Hong
dc.contributor.authorYang, Chao-Han Huck
dc.contributor.authorLiu, Fangyu
dc.contributor.authorTian, Meng
dc.contributor.authorLiu, Yi-Chieh
dc.contributor.authorWu, Ting-Wei
dc.contributor.authorLin, I-Hung
dc.contributor.authorWang, Kang
dc.contributor.authorMorikawa, Hiromasa
dc.contributor.authorChang, Hernghua
dc.contributor.authorTegner, Jesper
dc.contributor.authorWorring, Marcel
dc.date.accessioned2020-11-09T13:10:15Z
dc.date.available2020-11-09T13:10:15Z
dc.date.issued2020-11-01
dc.identifier.urihttp://hdl.handle.net/10754/665874
dc.description.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.
dc.description.sponsorshipThis work is supported by competitive research funding from King Abdullah University of Science and Technology (KAUST) and University of Amsterdam.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2011.00569
dc.rightsArchived with thanks to arXiv
dc.titleDeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
dc.typePreprint
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.eprint.versionPre-print
dc.contributor.institutionUniversity of Amsterdam.
dc.contributor.institutionGeorgia Institute of Technology.
dc.contributor.institutionUniversity of Cambridge.
dc.contributor.institutionDepartment of Ophthalmology, Bern University Hospital.
dc.contributor.institutionNational Taiwan University.
dc.contributor.institutionUniversity of California, Berkeley.
dc.contributor.institutionTri-Service General Hospital.
dc.contributor.institutionBeijing Friendship Hospital.
dc.identifier.arxivid2011.00569
kaust.personYang, Chao-Han Huck
kaust.personMorikawa, Hiromasa
kaust.personTegner, Jesper
refterms.dateFOA2020-11-09T13:11:07Z


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