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Clinical diagnosis of the eye is performed over multifarious data modalitiesincluding scalar clinical labels, vectorized biomarkers, two-dimensional fundusimages, and three-dimensional Optical Coherence Tomography (OCT) scans.Clinical practitioners use all available data modalities for diagnosing andtreating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema(DME). Enabling usage of machine learning algorithms within the ophthalmicmedical domain requires research into the relationships and interactionsbetween all relevant data over a treatment period. Existing datasets arelimited in that they neither provide data nor consider the explicitrelationship modeling between the data modalities. In this paper, we introducethe Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) datasetthat addresses the above limitation. This is the first OCT and near-IR fundusdataset that includes clinical labels, biomarker labels, disease labels, andtime-series patient treatment information from associated clinical trials. Thedataset consists of 1268 near-IR fundus images each with at least 49 OCT scans,and 16 biomarkers, along with 4 clinical labels and a disease diagnosis of DRor DME. In total, there are 96 eyes' data averaged over a period of at leasttwo years with each eye treated for an average of 66 weeks and 7 injections. Webenchmark the utility of OLIVES dataset for ophthalmic data as well as providebenchmarks and concrete research directions for core and emerging machinelearning paradigms within medical image analysis.

OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics