The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound
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| Publicat a: | Bioengineering vol. 12, no. 8 (2025), p. 855-889 |
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| Autor principal: | |
| Altres autors: | , , |
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MDPI AG
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3243983881 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5354 | ||
| 024 | 7 | |a 10.3390/bioengineering12080855 |2 doi | |
| 035 | |a 3243983881 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Blake, VanBerlo |u David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada | |
| 245 | 1 | |a The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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—a 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. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Medical imaging | ||
| 653 | |a COVID-19 | ||
| 653 | |a Machine learning | ||
| 653 | |a Pleural effusion | ||
| 653 | |a Data augmentation | ||
| 653 | |a Self-supervised learning | ||
| 653 | |a Preprocessing | ||
| 653 | |a Semantics | ||
| 653 | |a Pneumonia | ||
| 653 | |a Pipelines | ||
| 653 | |a Medical research | ||
| 653 | |a Effectiveness | ||
| 653 | |a Ultrasound | ||
| 653 | |a Object recognition | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Embedding | ||
| 700 | 1 | |a Hoey, Jesse |u David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada | |
| 700 | 1 | |a Wong, Alexander |u Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada | |
| 700 | 1 | |a Arntfield, Robert |u Schulich School of Medicine and Dentistry, Western University, London, ON N6A 3K7, Canada | |
| 773 | 0 | |t Bioengineering |g vol. 12, no. 8 (2025), p. 855-889 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3243983881/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3243983881/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3243983881/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |