OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
This dataset enables research into multimodal relationships for diagnosing eye diseases like Diabetic Retinopathy and Diabetic Macular Edema, but it is incremental as it fills a gap in existing data rather than proposing a new method.
The paper introduces the OLIVES dataset, which addresses the lack of multimodal ophthalmic data by providing the first dataset with OCT and near-IR fundus images, clinical and biomarker labels, and time-series treatment information from 96 eyes over at least two years, and benchmarks its utility for machine learning in medical image analysis.
Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. Clinical practitioners use all available data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between all relevant data over a treatment period. Existing datasets are limited in that they neither provide data nor consider the explicit relationship modeling between the data modalities. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitation. This is the first OCT and near-IR fundus dataset that includes clinical labels, biomarker labels, disease labels, and time-series patient treatment information from associated clinical trials. The dataset 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 DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. We benchmark the utility of OLIVES dataset for ophthalmic data as well as provide benchmarks and concrete research directions for core and emerging machine learning paradigms within medical image analysis.