Retinal disease diagnosis in OCT scans using a foundational model

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dc.authorid0009-0007-4212-5484
dc.authorid0000-0001-6309-4524
dc.authorid0000-0002-8649-6013
dc.authorid0000-0001-7013-5451
dc.authorid0000-0001-8619-8078
dc.authorid0009-0004-3598-4326
dc.authorid0000-0002-1793-2600
dc.contributor.authorNazlı, Muhammet Serdaren_US
dc.contributor.authorTurkan, Yaseminen_US
dc.contributor.authorTek, Faik Borayen_US
dc.contributor.authorToslak, Devrimen_US
dc.contributor.authorBulut, Mehmeten_US
dc.contributor.authorArpacı, Fatihen_US
dc.contributor.authorÖcal, Mevlüt Celalen_US
dc.date.accessioned2025-08-29T10:51:18Z
dc.date.available2025-08-29T10:51:18Z
dc.date.issued2025
dc.departmentIşık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Doktora Programıen_US
dc.departmentIşık University, School of Graduate Studies, Ph.D. in Computer Engineering
dc.descriptionThis study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 122E509.en_US
dc.description.abstractThis study examines the feasibility and performance of using single OCT slices from the OCTA-500 dataset to classify DR (Diabetic Retinopathy) and AMD (Age-Related Macular Degeneration) with a pre-trained transformer-based model (RETFound). The experiments revealed the effective adaptation capability of the pretrained model to the retinal disease classification problem. We further explored the impact of using different slices from the OCT volume, assessing the sensitivity of the results to the choice of a single slice (e.g., “middle slice”) and whether analyzing both horizontal and vertical cross-sectional slices could improve outcomes. However, deep neural networks are complex systems that do not indicate directly whether they have learned and generalized the disease appearance as human experts do. The original dataset lacked disease localization annotations. Therefore, we collected new disease classification and localization annotations from independent experts for a subset of OCTA-500 images. We compared RETFound’s explainability-based localization outputs with these newly collected annotations and found that the region attributions aligned well with the expert annotations. Additionally, we assessed the agreement and variability between experts and RETFound in classifying disease conditions. The Kappa values, ranging from 0.35 to 0.69, indicated moderate agreement among experts and between the experts and the model. The transformer-based RETFound model using single or multiple OCT slices, is an efficient approach to diagnosing AMD and DR.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumuen_US
dc.description.versionPublisher's Version
dc.identifier.citationNazlı, M. S., Turkan, Y., Tek, F. B., Toslak, D., Bulut, M., Arpacı, F. & Öcal, M. C. (2025). Retinal disease diagnosis in OCT scans using a foundational model. Paper presented at the Lecture Notes in Computer Science, 15618 LNCS, 208-220. doi:https://doi.org/10.1007/978-3-031-88220-3_15en_US
dc.identifier.doi10.1007/978-3-031-88220-3_15
dc.identifier.endpage220
dc.identifier.isbn9783031882197
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-105005655724
dc.identifier.scopusqualityQ3
dc.identifier.startpage208
dc.identifier.urihttps://hdl.handle.net/11729/6679
dc.identifier.urihttps://doi.org/10.1007/978-3-031-88220-3_15
dc.identifier.volume15618 LNCS
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTurkan, Yaseminen_US
dc.institutionauthorid0000-0001-6309-4524
dc.language.isoenen_US
dc.peerreviewedYes
dc.publicationstatusPublished
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAge related macula degenerationen_US
dc.subjectDiabetic retinopathyen_US
dc.subjectExplainabilityen_US
dc.subjectOCTen_US
dc.subjectRetinal disease diagnosisen_US
dc.subjectTransformeren_US
dc.subjectOphthalmologyen_US
dc.subjectAge-related macula degenerationsen_US
dc.subjectAge-related macular degenerationen_US
dc.subjectDiabetic retinopathyen_US
dc.subjectDisease diagnosisen_US
dc.subjectLocalisationen_US
dc.subjectRetinal diseaseen_US
dc.subjectRetinal disease diagnoseen_US
dc.subjectDeep neural networksen_US
dc.titleRetinal disease diagnosis in OCT scans using a foundational modelen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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