Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification
DC Field | Value | Language |
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dc.contributor.author | Hua, Cam-Hao | - |
dc.contributor.author | Thien Huynh-The | - |
dc.contributor.author | Kim, Kiyoung | - |
dc.contributor.author | Yu, Seung-Young | - |
dc.contributor.author | Thuong Le-Tien | - |
dc.contributor.author | Park, Gwang Hoon | - |
dc.contributor.author | Bang, Jaehun | - |
dc.contributor.author | Khan, Wajahat Ali | - |
dc.contributor.author | Bae, Sung-Ho | - |
dc.contributor.author | Lee, Sungyoung | - |
dc.date.accessioned | 2024-02-27T16:31:33Z | - |
dc.date.available | 2024-02-27T16:31:33Z | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 1386-5056 | - |
dc.identifier.issn | 1872-8243 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28188 | - |
dc.description.abstract | Background: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Electronic Medical Records (EMR) for acquiring such kind of DR-oriented knowledge through conventional machine learning techniques, which require an exhaustive job of engineering most impactful risk factors. Objective: In this paper, an approach of deep bimodal learning is introduced to leverage the performance of DR risk progression identification. Methods: In particular, we further involve valuable clinical information of fundus photography in addition to the aforementioned systemic attributes. Accordingly, a Trilogy of Skip-connection Deep Networks, namely Tri-SDN, is proposed to exhaustively exploit underlying relationships between the baseline and follow-up information of the fundus images and EMR-based attributes. Besides that, we adopt Skip-Connection Blocks as basis components of the Tri-SDN for making the end-to-end flow of signals more efficient during feedforward and backpropagation processes. Results: Through a 10-fold cross validation strategy on a private dataset of 96 diabetic mellitus patients, the proposed method attains superior performance over the conventional EMR-modality learning approach in terms of Accuracy (90.6%), Sensitivity (96.5%), Precision (88.7%), Specificity (82.1%), and Area Under Receiver Operating Characteristics (88.8%). Conclusions: The experimental results show that the proposed Tri-SDN can combine features of different modalities (i.e., fundus images and EMR-based numerical risk factors) smoothly and effectively during training and testing processes, respectively. As a consequence, with impressive performance of DR risk progression recognition, the proposed approach is able to help the ophthalmologists properly decide follow-up schedule and subsequent treatment plans. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ELSEVIER IRELAND LTD | - |
dc.title | Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification | - |
dc.type | Article | - |
dc.publisher.location | 아일랜드 | - |
dc.identifier.doi | 10.1016/j.ijmedinf.2019.07.005 | - |
dc.identifier.wosid | 000492149900010 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, v.132 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS | - |
dc.citation.volume | 132 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Health Care Sciences & Services | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Health Care Sciences & Services | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.subject.keywordPlus | DECISION-SUPPORT-SYSTEM | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordAuthor | Bimodal learning | - |
dc.subject.keywordAuthor | Diabetic Retinopathy risk progression | - |
dc.subject.keywordAuthor | EMR-based attributes | - |
dc.subject.keywordAuthor | Fundus photography | - |
dc.subject.keywordAuthor | Retinal fundus image | - |
dc.subject.keywordAuthor | Trilogy of skip-connection deep networks | - |
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