most citedExploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

172 citations · 172 across the 1 of their papers we have counts for

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eess.IV2019★ 172 cited

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl +5

Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological a…