grant
Distributed foundational models for multi-task learning in diabetic retinopathy
Organization UNIVERSITY OF NORTH CAROLINA CHARLOTTELocation CHARLOTTE, UNITED STATESPosted 1 May 2026Deadline 31 Mar 2030
NIHUS FederalResearch GrantFY2026AI basedAccuracy of DiagnosisAddressAffectAlgorithmsAngiogramAngiographyArteriesBiological MarkersBlindnessBlood VesselsChicagoClassificationClinicalClinical DataClinical ManagementDataData CompromisingData ReportingData SetDemocracyDevicesDiabetic RetinopathyDiagnosisDiagnosticDiffusionDisease ProgressionDoppler OCTEarly DiagnosisEnvironmentEvaluationFormosaIllinoisImageInstitutionIntellectual PropertyInterventionLabelLearningLifeMachine LearningMasksMethodsModelingOCT TomographyOCT angiographyOCTAOphthalmologistOptical Coherence TomographyPathway interactionsPatientsPerformancePrivacyProbabilistic ModelsProbability ModelsPrognosisProtocolProtocols documentationRepublic of ChinaResearchRetinal DiseasesRetinal DisorderSecureStagingStandardizationStatistical ModelsSystematicsTaiwanTechniquesTestingTrainingTreatment outcomeUniversitiesVeinsangiographic imagingartificial intelligence basedbio-markersbiologic markerbiomarkerclinical relevanceclinically relevantcohortcomparativedata collected in real worlddata diversitydata heterogeneitydata modelingdata privacydata representationdata representationsdata set heterogeneitydata sharingdataset heterogeneitydensitydepositorydiabeticdiabetic patientdiagnostic accuracydiffuseddiffusesdiffusingdiffusionsdisease classificationdisorder classificationdiverse dataearly detectionexperienceexperimentexperimental researchexperimental studyexperimentseye imagingfederated learningheterogeneous dataheterogeneous data setsheterogeneous datasetsheterogenous dataheterogenous data setsheterogenous datasetsimagingimprovedinnovateinnovationinnovativelearning networkmachine based learningmachine learning based frameworkmachine learning based methodmachine learning based modelmachine learning frameworkmachine learning methodmachine learning methodologiesmachine learning modelmacular edemamodel buildingmodel of datamodel the datamodeling of the datamulti-modal datamulti-modal datasetsmulti-modalitymulti-taskmulti-task learningmultimodal datamultimodal datasetsmultimodalitymultitaskmultitask learningnosologynovelocular imagingophthalmic imagingoptical Doppler tomographyoptical coherence Doppler tomographyoptical coherence tomography angiographypathwaypatient privacypreventpreventingprognosis modelprognostic modelproliferative diabetic retinopathypublic health relevancereal world datarepositoryretina diseaseretina disorderretinopathyrural arearural locationrural regionself supervisedself supervised learningself supervisionstatistical linear mixed modelsstatistical linear modelssuccesssupervised learningsupervised machine learningunder served areaunder served geographic areaunder served locationunder served regionunderserved areaunderserved geographic areaunderserved locationunderserved regionvascularvision lossvisual loss
Description preview
Abstract: This project aims to establish distributed federated learning (FL) approaches for multi-task training of
foundational machine learning (ML) models for diabetic retinopathy (DR), using multi-modal, real-world optical
coherence tomography (OCT) data (OCT cross-section, OCT angiography (OCTA), and OCT enface). DR is one
of the leading…
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