Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features
Mara Graziani, Vincent Andrearczyk and Henning Müller, Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
Abstract
Deep learning models, with billions of parameters, have a great potential for improving our daily life activities, particularly in assisting the diagnosis of medical images. The detection of plus disease in retinopathy of prematurity is an example at the edge between two treatment strategies where a difficult decision in the diagnosis may make the difference between the risk of blindness and complete recovery. Explaining the complex decision-making ...
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