Optical Coherence Tomography (OCT) Images

Published on: 2018-06-01
Version: 3
DOI: https://doi.org/10.17632/rscbjbr9sj.3
Contributors: Daniery Kermany, Kang Zhang, Michael Goldbaum
Datarows: 109,309

This dataset contains thousands of validated optical coherence tomography (OCT) images described and analyzed in "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning". The images are split into a training set and a testing set of independent patients. Images are labeled as (disease)-(randomized patient ID)-(image number by this patient) and split into 4 directories: CNV, DME, DRUSEN, and NORMAL.

Retinal OCT is an imaging technique used to capture high-resolution cross sections of the retinas of living patients. Approximately 30 million OCT scans are performed each year, and the analysis and interpretation of these images takes up a significant amount of time (Swanson and Fujimoto, 2017).

Related article: Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning (Kermany et al.)

Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images”, Mendeley Data, V3, doi: 10.17632/rscbjbr9sj.3
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