About: Explainable deep learning models in medical image analysis

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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	Explainable deep learning models in medical image analysis
Creator	Lakshminarayanan, Vasudevan Sengupta, Sourya Singh, Amitojdeep
source	ArXiv
abstract	Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those. However, the black-box nature of the algorithms has restricted clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.
has issue date	2020-05-28(xsd:dateTime)
has license	arxiv
sha1sum (hex)	af6af40155860ac2fd36680e63686473803b5f52
resource representing a document's title	Explainable deep learning models in medical image analysis
resource representing a document's body	covid:af6af40155860ac2fd36680e63686473803b5f52#body_text
is schema:about of	named entity 'The majority' named entity 'applications' named entity 'researcher' named entity 'model' named entity 'medical diagnostic' »more»

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