A systematic review of lightweight transformer models for medical image segmentation
Finding, assessing, and synthesizing studies on lightweight transformer models for medical picture segmentation is the goal of this SLR. Accuracy and efficiency in medical image processing and analysis are becoming more and more crucial as the amount of medical data increases. It has been demonstrat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2025/03/bioconf_ichbs2025_01036.pdf |
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| Summary: | Finding, assessing, and synthesizing studies on lightweight transformer models for medical picture segmentation is the goal of this SLR. Accuracy and efficiency in medical image processing and analysis are becoming more and more crucial as the amount of medical data increases. It has been demonstrated that lightweight transformer models have a lot of promise for producing precise and quick outcomes while using fewer computer resources. Several lightweight transformer models for medical picture segmentation have been examined in this paper. The findings demonstrate that, in comparison to traditional techniques, these models offer notable gains in medical image segmentation accuracy and efficiency. The need for improved generalization and testing on a wider range of datasets are among the difficulties noted. To overcome these obstacles and broaden the use of lightweight transformer models in diverse medical settings, more investigation is required. As a result, this review offers significant insights for future research and helpful advice for researchers and practitioners in creating and deploying lightweight transformer models for medical image segmentation. |
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| ISSN: | 2117-4458 |