Work

Detecting multiple retinal diseases in ultra-widefield fundus imaging and data-driven identification of informative regions with deep learning

Year: 2022

Type: article

Abstract: Advances in ultra-widefield retinal imaging have created a need for automated disease detection. Engelmann and colleagues develop a deep learning model for the detection of retinal diseases. They evaluate it under more realistic conditions than has been considered previously and investigate what regions of ultra-widefield images are important for the performance of such a model.

Source: Nature Machine Intelligence

DOI: 10.1038/s42256-022-00566-5

Authors: Justin Engelmann, Alice D. McTrusty, Ian J. C. MacCormick, Emma Pead, Amos Storkey, Miguel O. Bernabeu

Institutions: University of Edinburgh, NHS Education for Scotland, Centre for Inflammation Research

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $11,390