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Interpretable multimodal classification for age-related macular degeneration diagnosi

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dc.contributor.authorVairetti; Carla
dc.contributor.authorMaldonado, Sebastián
dc.contributor.authorCuitino; Loreto
dc.contributor.authorURZUA, CRISTHIAN ALEJANDRO
dc.date.accessioned2025-08-20T17:46:35Z
dc.date.available2025-08-20T17:46:35Z
dc.date.issued2024
dc.description.abstractExplainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to "unbox" the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources.
dc.description.versionVersión Publicada
dc.identifier.citationVairetti C, Maldonado S, Cuitino L, Urzua CA. Interpretable multimodal classification for age-related macular degeneration diagnosis. PLoS One. 2024 Nov 11;19(11):e0311811. doi: 10.1371/journal.pone.0311811. Erratum in: PLoS One. 2025 Jul 29;20(7):e0329294. doi: 10.1371/journal.pone.0329294
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0311811
dc.identifier.urihttps://hdl.handle.net/11447/10195
dc.language.isoen
dc.subjectMacular Degeneration* / classification
dc.subjectImage Processing
dc.subjectComputer-Assisted / methods
dc.subjectArtificial Intelligence
dc.subjectDeep Learning
dc.titleInterpretable multimodal classification for age-related macular degeneration diagnosi
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourcePloS one
dspace.entity.typePublication
relation.isAuthorOfPublicationd1e93d92-ff40-419a-a8dd-1f0a39cd9e5e
relation.isAuthorOfPublication.latestForDiscoveryd1e93d92-ff40-419a-a8dd-1f0a39cd9e5e

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