{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:37:17Z","timestamp":1760060237417,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada (NSERC)","doi-asserted-by":"publisher","award":["186945"],"award-info":[{"award-number":["186945"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Data augmentation is a central component of joint embedding self-supervised learning (SSL). Approaches that work for natural images may not always be effective in medical imaging tasks. This study systematically investigated the impact of data augmentation and preprocessing strategies in SSL for lung ultrasound. Three data augmentation pipelines were assessed: (1) a baseline pipeline commonly used across imaging domains, (2) a novel semantic-preserving pipeline designed for ultrasound, and (3) a distilled set of the most effective transformations from both pipelines. Pretrained models were evaluated on multiple classification tasks: B-line detection, pleural effusion detection, and COVID-19 classification. Experiments revealed that semantics-preserving data augmentation resulted in the greatest performance for COVID-19 classification\u2014a diagnostic task requiring global image context. Cropping-based methods yielded the greatest performance on the B-line and pleural effusion object classification tasks, which require strong local pattern recognition. Lastly, semantics-preserving ultrasound image preprocessing resulted in increased downstream performance for multiple tasks. Guidance regarding data augmentation and preprocessing strategies was synthesized for developers working with SSL in ultrasound.<\/jats:p>","DOI":"10.3390\/bioengineering12080855","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T15:30:52Z","timestamp":1754667052000},"page":"855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1593-8275","authenticated-orcid":false,"given":"Blake","family":"VanBerlo","sequence":"first","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5340-2204","authenticated-orcid":false,"given":"Jesse","family":"Hoey","sequence":"additional","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Wong","sequence":"additional","affiliation":[{"name":"Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4954-487X","authenticated-orcid":false,"given":"Robert","family":"Arntfield","sequence":"additional","affiliation":[{"name":"Schulich School of Medicine and Dentistry, Western University, London, ON N6A 3K7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"54310","DOI":"10.1109\/ACCESS.2021.3071301","article-title":"Deep learning in medical ultrasound image analysis: A review","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, Q., Wei, J., Hao, X., Kong, D., Yu, X., Jiang, T., Xi, J., Cai, W., Luo, Y., and Jing, X. 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Syst."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/8\/855\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:26:54Z","timestamp":1760034414000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/8\/855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,8]]},"references-count":46,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["bioengineering12080855"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12080855","relation":{},"ISSN":["2306-5354"],"issn-type":[{"type":"electronic","value":"2306-5354"}],"subject":[],"published":{"date-parts":[[2025,8,8]]}}}