Safeguarding personal information is paramount for healthcare data sharing, a challenging issue without any silver bullet thus far. We study the prospect of a recent deep-learning advent, dataset condensation (DC), in sharing healthcare data for AI research, and the results are promising. The condensed data abstracts original records and irreversibly conceals individual-level knowledge to achieve a bona fide de-identification, which permits free sharing. Moreover, the original deep-learning utilities are well preserved in the condensed data with compressed volume and accelerated model convergences. In PhysioNet-2012, a condensed dataset of 20 samples can orient deep models attaining 80.3% test AUC of mortality prediction (versus 85.8% of 5120 original records), an inspiring discovery generalised to MIMIC-III and Coswara datasets. We also interpret the inhere privacy protections of DC through theoretical analysis and empirical evidence. Dataset condensation opens a new gate to sharing healthcare data for AI research with multiple desirable traits.

翻译：保护个人信息对于医疗数据共享至关重要，但目前尚无完美解决方案。我们研究了深度学习领域的新兴技术——数据集压缩（DC）在医疗数据共享用于AI研究中的应用前景，结果令人振奋。压缩后的数据能够提取原始记录的关键特征，并通过不可逆的方式隐藏个体层面的知识，实现真正的去标识化，从而允许自由共享。此外，原始深度学习模型的效用被完好保留，同时数据量级压缩、模型收敛速度提升。在PhysioNet-2012数据集中，仅含20个样本的压缩数据集可引导深度学习模型在死亡率预测任务中达到80.3%的测试AUC（相比之下，原始5120个样本的AUC为85.8%），这一鼓舞人心的发现同样在MIMIC-III和Coswara数据集中得到验证。我们还通过理论分析和实验证据阐释了DC内在的隐私保护机制。数据集压缩为医疗数据共享用于AI研究开辟了一条兼具多重优势的新路径。