Data-effective learning aims to use data in the most impactful way to train
AI models, which involves strategies that focus on data quality rather than
quantity, ensuring the data used for training has high informational value.
Data-effective learning plays a profound role in accelerating AI training,
reducing computational costs, and saving data storage, which is very important
as the volume of medical data in recent years has grown beyond many people's
expectations. However, due to the lack of standards and comprehensive
benchmark, research on medical data-effective learning is poorly studied. To
address this gap, our paper introduces a comprehensive benchmark specifically
for evaluating data-effective learning in the medical field. This benchmark
includes a dataset with millions of data samples from 31 medical centers
(DataDEL), a baseline method for comparison (MedDEL), and a new evaluation
metric (NormDEL) to objectively measure data-effective learning performance.
Our extensive experimental results show the baseline MedDEL can achieve
performance comparable to the original large dataset with only 5% of the data.
Establishing such an open data-effective learning benchmark is crucial for the
medical AI research community because it facilitates efficient data use,
promotes collaborative breakthroughs, and fosters the development of
cost-effective, scalable, and impactful healthcare solutions. The project can
be accessed at
this https URL

我们的研究论文针对数据有效学习在医学领域的研究空白，引入了一种全面的基准，用于评估医学领域中的数据有效学习。该基准包括来自 31 个医疗中心的数百万数据样本的数据集（DataDEL），用于对比的基线方法（MedDEL），以及客观衡量数据有效学习性能的新评估指标（NormDEL）。我们广泛的实验结果表明，基线方法 MedDEL 仅使用 5% 的数据就可以达到与原始大数据集相当的性能。建立这样一个开放的数据有效学习基准对医学人工智能研究社区至关重要，因为它促进了高效数据利用、推动协作突破和发展成本效益高、可扩展且有影响力的医疗解决方案。