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DC Field Value Language Yang, Hyun Lim ko Kim, Jong Jin ko Kim, Jong Ho ko Kang, Yong Koo ko Park, Dong Ho ko Park, Han Sang ko Kim, Hong Kyun ko Kim, Min-Soo ko 2019-04-18T06:26:45Z - 2019-04-18T06:26:45Z - 2019-04-18 - 2019-04 -
dc.identifier.citation PLoS ONE, v.14, no.4 -
dc.identifier.issn 1932-6203 -
dc.identifier.uri -
dc.description.abstract Age-related macular degeneration (AMD) is the main cause of irreversible blindness among the elderly and require early diagnosis to prevent vision loss, and careful treatment is essential. Optical coherence tomography (OCT), the most commonly used imaging method in the retinal area for the diagnosis of AMD, is usually interpreted by a clinician, and OCT can help diagnose disease on the basis of the relevant diagnostic criteria, but these judgments can be somewhat subjective. We propose an algorithm for the detection of AMD based on a weakly supervised convolutional neural network (CNN) model to support computer-aided diagnosis (CAD) system. Our main contributions are the following three things. (1) We propose a concise CNN model for OCT images, which outperforms the existing large CNN models using VGG16 and GoogLeNet architectures. (2) We propose an algorithm called Expressive Gradients (EG) that extends the existing Integrated Gradients (IG) algorithm so as to exploit not only the input-level attribution map, but also the high-level attribution maps. Due to enriched gradients, EG can highlight suspicious regions for diagnosis of AMD better than the guided-backpropagation method and IG. (3) Our method provides two visualization options: Overlay and top-k bounding boxes, which would be useful for CAD. Through experimental evaluation using 10,100 clinical OCT images from AMD patients, we demonstrate that our EG algorithm outperforms the IG algorithm in terms of localization accuracy and also outperforms the existing object detection methods in terms of class accuracy. © 2019 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
dc.language English -
dc.publisher Public Library of Science -
dc.title Weakly supervised lesion localization for age-related macular degeneration detection using optical coherence tomography images -
dc.type Article -
dc.identifier.doi 10.1371/journal.pone.0215076 -
dc.identifier.wosid 000463487500032 -
dc.identifier.scopusid 2-s2.0-85064068425 -
dc.type.local Article(Overseas) -
dc.type.rims ART -
dc.description.journalClass 1 -
dc.contributor.nonIdAuthor Kim, Jong Jin -
dc.contributor.nonIdAuthor Kim, Jong Ho -
dc.contributor.nonIdAuthor Kang, Yong Koo -
dc.contributor.nonIdAuthor Park, Dong Ho -
dc.contributor.nonIdAuthor Park, Han Sang -
dc.contributor.nonIdAuthor Kim, Hong Kyun -
dc.identifier.citationVolume 14 -
dc.identifier.citationNumber 4 -
dc.identifier.citationTitle PLoS ONE -
dc.type.journalArticle Article -
dc.description.isOpenAccess Y -
dc.subject.keywordPlus CLASSIFICATION -
dc.contributor.affiliatedAuthor Kim, Min-Soo -
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