Convolutional Network With Twofold Feature Augmentation for Diabetic Retinopathy Recognition From Multi-Modal Images
DC Field | Value | Language |
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dc.contributor.author | Hua, Cam-Hao | - |
dc.contributor.author | Kim, Kiyoung | - |
dc.contributor.author | Thien Huynh-The | - |
dc.contributor.author | You, Jong In | - |
dc.contributor.author | Yu, Seung-Young | - |
dc.contributor.author | Le-Tien, Thuong | - |
dc.contributor.author | Bae, Sung-Ho | - |
dc.contributor.author | Lee, Sungyoung | - |
dc.date.accessioned | 2024-02-27T16:31:47Z | - |
dc.date.available | 2024-02-27T16:31:47Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.issn | 2168-2208 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28269 | - |
dc.description.abstract | Objective: With the scenario of limited labeled dataset, this paper introduces a deep learning-based approach that leverages Diabetic Retinopathy (DR) severity recognition performance using fundus images combined with wide-field swept-source optical coherence tomography angiography (SS-OCTA). Methods: The proposed architecture comprises a backbone convolutional network associated with a Twofold Feature Augmentation mechanism, namely TFA-Net. The former includes multiple convolution blocks extracting representational features at various scales. The latter is constructed in a two-stage manner, i.e., the utilization of weight-sharing convolution kernels and the deployment of a Reverse Cross-Attention (RCA) stream. Results: The proposed model achieves a Quadratic Weighted Kappa rate of 90.2% on the small-sized internal KHUMC dataset. The robustness of the RCA stream is also evaluated by the single-modal Messidor dataset, of which the obtained mean Accuracy (94.8%) and Area Under Receiver Operating Characteristic (99.4%) outperform those of the state-of-the-arts significantly. Conclusion: Utilizing a network strongly regularized at feature space to learn the amalgamation of different modalities is of proven effectiveness. Thanks to the widespread availability of multi-modal retinal imaging for each diabetes patient nowadays, such approach can reduce the heavy reliance on large quantity of labeled visual data. Significance: Our TFA-Net is able to coordinate hybrid information of fundus photos and wide-field SS-OCTA for exhaustively exploiting DR-oriented biomarkers. Moreover, the embedded feature-wise augmentation scheme can enrich generalization ability efficiently despite learning from small-scale labeled data. | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Convolutional Network With Twofold Feature Augmentation for Diabetic Retinopathy Recognition From Multi-Modal Images | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/JBHI.2020.3041848 | - |
dc.identifier.wosid | 000678341200032 | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.25, no.7, pp 2686 - 2697 | - |
dc.citation.title | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.volume | 25 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 2686 | - |
dc.citation.endPage | 2697 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.subject.keywordPlus | ANGIOGRAPHY | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordAuthor | Streaming media | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Diabetes | - |
dc.subject.keywordAuthor | Retinopathy | - |
dc.subject.keywordAuthor | Retina | - |
dc.subject.keywordAuthor | Image recognition | - |
dc.subject.keywordAuthor | Bioinformatics | - |
dc.subject.keywordAuthor | Convolutional network | - |
dc.subject.keywordAuthor | diabetic retinopathy recognition | - |
dc.subject.keywordAuthor | fundus photograph | - |
dc.subject.keywordAuthor | multi-modal images | - |
dc.subject.keywordAuthor | SS-OCT angiography | - |
dc.subject.keywordAuthor | twofold feature augmentation | - |
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