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| http://dx.doi.org/10.1016/j.cgh.2019.09.001 | DOI Listing |
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Department of Ophthalmology, Hospital Universitario de Canarias, Carretera Ofra S/N, La Laguna, Santa Cruz de Tenerife, 38320, SPAIN.
This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.
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Department of Surgery, Section of Otolaryngology-Head and Neck Surgery, University of Chicago Medicine, Chicago, Illinois.
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