{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T01:09:57Z","timestamp":1728176997301},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"macau science and technology development","award":["0056\/2020\/AFJ"],"award-info":[{"award-number":["0056\/2020\/AFJ"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image\/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3\u201310% higher than that of the existing models.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-021-00704-2","type":"journal-article","created":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T11:02:53Z","timestamp":1637578973000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients"],"prefix":"10.1186","volume":"21","author":[{"given":"Rui","family":"Miao","sequence":"first","affiliation":[]},{"given":"Xin","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Sheng-Li","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Sio-Long","family":"Lo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,22]]},"reference":[{"key":"704_CR1","doi-asserted-by":"crossref","unstructured":"COVID TC, Team R. 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