{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T09:00:39Z","timestamp":1766221239028,"version":"3.48.0"},"reference-count":28,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Detecting malignancy in pulmonary nodules holds significant clinical importance, yet existing image classification methods often struggle with inadequate feature integration and ineffective loss functions. This study proposes two innovative strategies to address these limitations: first, we introduce a multiscale feature weighted fusion technique that enhances the integration of features across different scales, allowing the model to prioritize critical pixel locations essential for accurate diagnosis. Second, we combine contrastive loss with binary cross-entropy within our training framework to improve learning from both similarities and differences among paired samples, which fosters better discrimination between similar nodules while maintaining sensitivity to variations across classes. Besides, our proposed methodologies demonstrate promising performance improvements in detecting pulmonary nodule malignancy, leading to enhanced performance and reliability compared to conventional approaches.<\/jats:p>","DOI":"10.1515\/comp-2025-0030","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T09:55:48Z","timestamp":1759571748000},"source":"Crossref","is-referenced-by-count":0,"title":["A multiscale and dual-loss network for pulmonary nodule classification"],"prefix":"10.1515","volume":"15","author":[{"given":"Ping","family":"Zhang","sequence":"first","affiliation":[{"name":"Qingdao Central Hospital, University of Health and Rehabilitation Sciences , Qingdao , 266042 , P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Li","sequence":"additional","affiliation":[{"name":"Qingdao University of Science and Technology , Qingdao , 266061 , P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Mao","sequence":"additional","affiliation":[{"name":"Qingdao Central Hospital, University of Health and Rehabilitation Sciences , Qingdao , 266042 , P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"2025122008514898505_j_comp-2025-0030_ref_001","doi-asserted-by":"crossref","unstructured":"A. McWilliams, M. C. Tammemagi, J. R. Mayo, H. Roberts, G. Liu, K. Soghrati, et al., \u201cProbability of cancer in pulmonary nodules detected on first screening CT,\u201d New Engl. J. Med., vol. 369, no. 10, pp. 910\u2013919, 2013.","DOI":"10.1056\/NEJMoa1214726"},{"key":"2025122008514898505_j_comp-2025-0030_ref_002","doi-asserted-by":"crossref","unstructured":"A. E. Prosper, M. N. Kammer, F. Maldonado, D. R. Aberle, and W. 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Koehn, et al., \u201cMachine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT,\u201d Med. Phys., vol. 46, no. 7, pp. 3207\u20133216, 2019, http:\/\/dx.doi.org\/10.1002\/mp.13592.","DOI":"10.1002\/mp.13592"},{"key":"2025122008514898505_j_comp-2025-0030_ref_010","doi-asserted-by":"crossref","unstructured":"A. Yamada, A. Teramoto, M. Hoshi, H. Toyama, K. Imaizumi, K. Saito, and H. Fujita, \u201cHybrid scheme for automated classification of pulmonary nodules using pet\/CT images and patient information,\u201d Appl. Sci., vol. 10, no. 2, 4225, 2020, http:\/\/dx.doi.org\/10.3390\/app10124225.","DOI":"10.3390\/app10124225"},{"key":"2025122008514898505_j_comp-2025-0030_ref_011","doi-asserted-by":"crossref","unstructured":"K. Chen, Y. Nie, S. Park, K. Zhang, Y. Zhang, Y. Liu, et al., \u201cDevelopment and validation of machine learning-based model for the prediction of malignancy in multiple pulmonary nodules: Analysis from multicentric cohorts,\u201d Clin. Cancer Res., vol. 27, no. 8, pp. 2255\u20132265, 2021, http:\/\/dx.doi.org\/10.1158\/1078-0432.ccr-20-4007.","DOI":"10.1158\/1078-0432.CCR-20-4007"},{"key":"2025122008514898505_j_comp-2025-0030_ref_012","doi-asserted-by":"crossref","unstructured":"M. Liu, Z. Zhou, F. Liu, M. Wang, Y. Wang, M. Gao, et al., \u201cCt and CEA-based machine learning model for predicting malignant pulmonary nodules,\u201d Cancer Sci., vol. 113, no. 12, pp. 4363\u20134373, 2022. http:\/\/dx.doi.org\/10.1111\/cas.15561.","DOI":"10.1111\/cas.15561"},{"key":"2025122008514898505_j_comp-2025-0030_ref_013","doi-asserted-by":"crossref","unstructured":"H. Wang, T. Zhao, L. C. Li, H. Pan, W. Liu, H. Gao, et al., \u201cA hybrid CNN feature model for pulmonary nodule malignancy risk differentiation,\u201d J. X-Ray Sci. 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