{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:03:44Z","timestamp":1771067024046,"version":"3.50.1"},"reference-count":43,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62101318"],"award-info":[{"award-number":["62101318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6210010007"],"award-info":[{"award-number":["6210010007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["20DZ2220400"],"award-info":[{"award-number":["20DZ2220400"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these methods generally target one particular lesion type, that is, pulmonary nodules. In this work, we intend to develop and evaluate a novel deep learning method for a more challenging task, detecting various benign and malignant mediastinal lesions with wide variations in sizes, shapes, intensities, and locations in chest CT images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our method for mediastinal lesion detection contains two main stages: (a) size\u2010adaptive lesion candidate detection followed by (b) false\u2010positive (FP) reduction and benign\u2013malignant classification. For candidate detection, an anchor\u2010free and one\u2010stage detector, namely 3D\u2010CenterNet is designed to locate suspicious regions (i.e., candidates with various sizes) within the mediastinum. Then, a 3D\u2010SEResNet\u2010based classifier is used to differentiate FPs, benign lesions, and malignant lesions from the candidates.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We evaluate the proposed method by conducting five\u2010fold cross\u2010validation on a relatively large\u2010scale dataset, which consists of data collected on 1136 patients from a grade A tertiary hospital. The method can achieve sensitivity scores of 84.3% \u00b1 1.9%, 90.2% \u00b1 1.4%, 93.2% \u00b1 0.8%, and 93.9% \u00b1 1.1%, respectively, in finding all benign and malignant lesions at 1\/8, 1\/4, \u00bd, and 1 FPs per scan, and the accuracy of benign\u2013malignant classification can reach up to 78.7% \u00b1 2.5%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed method can effectively detect mediastinal lesions with various sizes, shapes, and locations in chest CT images. It can be integrated into most existing pulmonary lesion detection systems to promote their clinical applications. The method can also be readily extended to other similar 3D lesion detection tasks.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15804","type":"journal-article","created":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T09:57:01Z","timestamp":1654941421000},"page":"7222-7236","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Size\u2010adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset"],"prefix":"10.1002","volume":"49","author":[{"given":"Jun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Computing Science Zhejiang University City College  Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiawei","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Zhejiang University  Hangzhou 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