About: Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia
Creator	Hussain, Faraz Khan, Nazar Velasquez, Alvaro Verenich, Edward
source	ArXiv
abstract	Trust in predictions made by machine learning models is increased if the model generalizes well on previously unseen samples and when inference is accompanied by cogent explanations of the reasoning behind predictions. In the image classification domain, generalization can be assessed through accuracy, sensitivity, and specificity. Explainability can be assessed by how well the model localizes the object of interest within an image. However, both generalization and explainability through localization are degraded in scenarios with significant overlap between classes. We propose a method based on binary expert networks that enhances the explainability of image classifications through better localization by mitigating the model uncertainty induced by class overlap. Our technique performs discriminative localization on images that contain features with significant class overlap, without explicitly training for localization. Our method is particularly promising in real-world class overlap scenarios, such as COVID-19 and pneumonia, where expertly labeled data for localization is not readily available. This can be useful for early, rapid, and trustworthy screening for COVID-19.
has issue date	2020-08-06(xsd:dateTime)
has license	arxiv
sha1sum (hex)	c1a25c1e9fb89674db2e32feef5056b09b7edee8
resource representing a document's title	Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia
resource representing a document's body	covid:c1a25c1e9fb89674db2e32feef5056b09b7edee8#body_text
is schema:about of	named entity 'mitigating' named entity 'model' named entity 'classifications' named entity 'binary' named entity 'Classification' »more»

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