Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/34897
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dc.contributor.authorJin Yen_US
dc.contributor.authorKondov Ben_US
dc.contributor.authorKondov Gen_US
dc.contributor.authorSinghal Sen_US
dc.contributor.authorNie Shen_US
dc.contributor.authorGruev Ven_US
dc.date.accessioned2026-02-17T12:38:15Z-
dc.date.available2026-02-17T12:38:15Z-
dc.date.issued2025-03-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/34897-
dc.description.abstractSingle-chip imaging devices with vertically stacked photodiodes and pixelated spectral filters enhance multi-dye imaging techniques for cancer surgeries. However, this advancement sacrifices spatial resolution. To address this issue, we have created a deep convolutional neural network designed to demosaic color and NIR channels, and its effectiveness has been confirmed through testing on both preclinical and clinical datasets. Keywords: CNN, demosaicing, near infrared imaging, image guided surgery, cancer surgeryen_US
dc.language.isoenen_US
dc.publisherSPIE Digital Libraryen_US
dc.relation.ispartofAdvanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXIIIen_US
dc.subjectCNNen_US
dc.subjectdemosaicingen_US
dc.subjectnear infrared imagingen_US
dc.subjectimage guided surgeryen_US
dc.subjectcancer surgeryen_US
dc.titleCNN-based demosaicing for labeled fluorescence cancer intraoperative imaging with visible-NIR sensorsen_US
dc.typeProceeding articleen_US
dc.relation.conferenceProc. SPIE 13306, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXIII, 133060B (20 March 2025)en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Medicine-
crisitem.author.deptFaculty of Medicine-
Appears in Collections:Faculty of Medicine: Conference papers
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