用特征指纹实现医疗影像AI知识共享，打破信息孤岛。
Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI
- 通过特征分布指纹表征数据集，量化任务相似性。
- 在71个任务、12种模态上验证，提升知识迁移效果。
- 适合希望高效复用模型与数据的医疗AI研究者。
医疗影像AI正快速向临床转化，但研究存在知识孤岛：知识分散于论文中，大量细节未公开，且隐私法规限制数据共享。为推动AI民主化，我们提出一种安全的知识迁移框架，核心是数据集的'指纹'——结构化的特征分布表示，可量化任务相似性。我们在71个不同任务和12种医学影像模态上测试了神经网络架构、预训练、增强策略及多任务学习的迁移。综合分析表明，该方法优于传统方法，能更准确识别相关知识，促进协作建模。本框架有助于推动医疗影像AI的普及与科学进步。
原文摘要 · Abstract (English)
The field of medical imaging AI is currently undergoing rapid transformations, with methodical research increasingly translated into clinical practice. Despite these successes, research suffers from knowledge silos, hindering collaboration and progress: Existing knowledge is scattered across publications and many details remain unpublished, while privacy regulations restrict data sharing. In the spirit of democratizing of AI, we propose a framework for secure knowledge transfer in the field of medical image analysis. The key to our approach is dataset "fingerprints", structured representations of feature distributions, that enable quantification of task similarity. We tested our approach across 71 distinct tasks and 12 medical imaging modalities by transferring neural architectures, pretraining, augmentation policies, and multi-task learning. According to comprehensive analyses, our method outperforms traditional methods for identifying relevant knowledge and facilitates collaborative model training. Our framework fosters the democratization of AI in medical imaging and could become a valuable tool for promoting faster scientific advancement.
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