General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis
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| Publicat a: | Nature Communications vol. 16, no. 1 (2025), p. 2097 |
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| Publicat: |
Nature Publishing Group
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3172628883 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2041-1723 | ||
| 024 | 7 | |a 10.1038/s41467-025-57427-z |2 doi | |
| 035 | |a 3172628883 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 145839 |2 nlm | ||
| 245 | 1 | |a General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The foundation model, trained on extensive and diverse datasets, has shown strong performance across numerous downstream tasks. Nevertheless, its application in the medical domain is significantly hindered by issues such as data volume, heterogeneity, and privacy concerns. Therefore, we propose the Vision Foundation Model General Lightweight (VFMGL) framework, which facilitates the decentralized construction of expert clinical models for various medical tasks. The VFMGL framework transfers general knowledge from large-parameter vision foundation models to construct lightweight, robust expert clinical models tailored to specific medical tasks. Through extensive experiments and analyses across a range of medical tasks and scenarios, we demonstrate that VFMGL achieves superior performance in both medical image classification and segmentation tasks, effectively managing the challenges posed by data heterogeneity. These results underscore the potential of VFMGL in advancing the efficacy and reliability of AI-driven medical diagnostics.Identifying and segmenting medical images plays a crucial role in advancing precision cancer treatment. This study proposes a Vision Foundation Model General Lightweight (VFMGL) framework, which facilitates the decentralized construction of expert clinical models for various medical image tasks. | |
| 653 | |a Image classification | ||
| 653 | |a Image processing | ||
| 653 | |a Image analysis | ||
| 653 | |a Heterogeneity | ||
| 653 | |a Models | ||
| 653 | |a Image segmentation | ||
| 653 | |a Medical imaging | ||
| 653 | |a Cancer therapies | ||
| 653 | |a Language | ||
| 653 | |a Datasets | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Gynecology | ||
| 653 | |a Lymphatic system | ||
| 653 | |a Knowledge | ||
| 653 | |a Hospitals | ||
| 653 | |a Natural language processing | ||
| 653 | |a Endometrial cancer | ||
| 653 | |a Privacy | ||
| 653 | |a Economic | ||
| 773 | 0 | |t Nature Communications |g vol. 16, no. 1 (2025), p. 2097 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3172628883/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3172628883/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |