{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T08:12:43Z","timestamp":1747210363820,"version":"3.40.5"},"reference-count":0,"publisher":"University of Porto","issue":"4","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["UPjeng"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.24840\/2183-6493_007.004_0002","type":"journal-article","created":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T12:23:18Z","timestamp":1637929398000},"page":"16-32","source":"Crossref","is-referenced-by-count":0,"title":["Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification"],"prefix":"10.24840","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4856-138X","authenticated-orcid":false,"given":"Joana","family":"Rocha","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4319-738X","authenticated-orcid":false,"given":"Ana Maria","family":"Mendon\u00e7a","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5317-6275","authenticated-orcid":false,"given":"Aur\u00e9lio","family":"Campilho","sequence":"additional","affiliation":[]}],"member":"10468","published-online":{"date-parts":[[2021,11,26]]},"container-title":["U.Porto Journal of Engineering"],"original-title":[],"link":[{"URL":"https:\/\/journalengineering.fe.up.pt\/index.php\/upjeng\/article\/download\/2183-6493_007-004_0002\/565","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journalengineering.fe.up.pt\/index.php\/upjeng\/article\/download\/2183-6493_007-004_0002\/565","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T12:23:32Z","timestamp":1637929412000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalengineering.fe.up.pt\/index.php\/upjeng\/article\/view\/2183-6493_007-004_0002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,26]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,11,26]]}},"URL":"https:\/\/doi.org\/10.24840\/2183-6493_007.004_0002","relation":{},"ISSN":["2183-6493"],"issn-type":[{"type":"electronic","value":"2183-6493"}],"subject":[],"published":{"date-parts":[[2021,11,26]]}}}