Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the agg...

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Autores principales:	Joana Rocha, Ana Maria Mendonça, Aurélio Campilho
Formato:	article
Lenguaje:	EN
Publicado:	Universidade do Porto 2021
Materias:	computer-aided diagnosis deep neural network medical imaging radiology thorax Engineering (General). Civil engineering (General) TA1-2040 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/cc58f799c99044c88c71649fc64669d4
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id	oai:doaj.org-article:cc58f799c99044c88c71649fc64669d4
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spelling	oai:doaj.org-article:cc58f799c99044c88c71649fc64669d42021-11-26T12:34:57ZReview on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification2183-649310.24840/2183-6493_007.004_0002https://doaj.org/article/cc58f799c99044c88c71649fc64669d42021-11-01T00:00:00Zhttps://journalengineering.fe.up.pt/index.php/upjeng/article/view/774https://doaj.org/toc/2183-6493Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.Joana RochaAna Maria MendonçaAurélio CampilhoUniversidade do Portoarticlecomputer-aided diagnosisdeep neural networkmedical imagingradiologythoraxEngineering (General). Civil engineering (General)TA1-2040Technology (General)T1-995ENU.Porto Journal of Engineering, Vol 7, Iss 4, Pp 16-32 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	computer-aided diagnosis deep neural network medical imaging radiology thorax Engineering (General). Civil engineering (General) TA1-2040 Technology (General) T1-995
spellingShingle	computer-aided diagnosis deep neural network medical imaging radiology thorax Engineering (General). Civil engineering (General) TA1-2040 Technology (General) T1-995 Joana Rocha Ana Maria Mendonça Aurélio Campilho Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification
description	Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.
format	article
author	Joana Rocha Ana Maria Mendonça Aurélio Campilho
author_facet	Joana Rocha Ana Maria Mendonça Aurélio Campilho
author_sort	Joana Rocha
title	Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification
title_short	Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification
title_full	Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification
title_fullStr	Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification
title_full_unstemmed	Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification
title_sort	review on deep learning methods for chest x-ray based abnormality detection and thoracic pathology classification
publisher	Universidade do Porto
publishDate	2021
url	https://doaj.org/article/cc58f799c99044c88c71649fc64669d4
work_keys_str_mv	AT joanarocha reviewondeeplearningmethodsforchestxraybasedabnormalitydetectionandthoracicpathologyclassification AT anamariamendonca reviewondeeplearningmethodsforchestxraybasedabnormalitydetectionandthoracicpathologyclassification AT aureliocampilho reviewondeeplearningmethodsforchestxraybasedabnormalitydetectionandthoracicpathologyclassification
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Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

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