From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| author | Jiang, Yuncheng Feng, Chun-Mei Ren, Jinke Wei, Jun Zhang, Zixun Hu, Yiwen Liu, Yunbi Sun, Rui Tang, Xuemei Du, Juan Wan, Xiang Xu, Yong Du, Bo Gao, Xin Wang, Guangyu Zhou, Shaohua Cui, Shuguang Li, Zhen |
| author_facet | Jiang, Yuncheng Feng, Chun-Mei Ren, Jinke Wei, Jun Zhang, Zixun Hu, Yiwen Liu, Yunbi Sun, Rui Tang, Xuemei Du, Juan Wan, Xiang Xu, Yong Du, Bo Gao, Xin Wang, Guangyu Zhou, Shaohua Cui, Shuguang Li, Zhen |
| contents | Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on physician expertise and is often hampered by suboptimal image quality, leading to potential diagnostic errors. While artificial intelligence (AI) offers a promising solution to enhance clinical diagnosis by detecting abnormalities across various imaging modalities, existing AI methods for ultrasound face two major challenges. First, they typically require vast amounts of labeled medical data, raising serious concerns regarding patient privacy. Second, most models are designed for specific tasks, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve (AUROC) of 0.927 for disease diagnosis and a dice similarity coefficient (DSC) of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers (4-8 years of experience) and matches the performance of expert-level sonographers (10+ years of experience) in the joint diagnosis of 8 common systemic diseases.c These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking a significant advancement in AI-driven ultrasound imaging for future clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16380 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning Jiang, Yuncheng Feng, Chun-Mei Ren, Jinke Wei, Jun Zhang, Zixun Hu, Yiwen Liu, Yunbi Sun, Rui Tang, Xuemei Du, Juan Wan, Xiang Xu, Yong Du, Bo Gao, Xin Wang, Guangyu Zhou, Shaohua Cui, Shuguang Li, Zhen Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on physician expertise and is often hampered by suboptimal image quality, leading to potential diagnostic errors. While artificial intelligence (AI) offers a promising solution to enhance clinical diagnosis by detecting abnormalities across various imaging modalities, existing AI methods for ultrasound face two major challenges. First, they typically require vast amounts of labeled medical data, raising serious concerns regarding patient privacy. Second, most models are designed for specific tasks, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve (AUROC) of 0.927 for disease diagnosis and a dice similarity coefficient (DSC) of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers (4-8 years of experience) and matches the performance of expert-level sonographers (10+ years of experience) in the joint diagnosis of 8 common systemic diseases.c These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking a significant advancement in AI-driven ultrasound imaging for future clinical applications. |
| title | From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16380 |