Training medical AI algorithms requires large volumes of accurately labeled
datasets, which are difficult to obtain in the real world. Synthetic images
generated from deep generative models can help alleviate the data scarcity
problem, but their effectiveness relies on their fidelity to real-world images.
Typically, researchers select synthesis models based on image quality
measurements, prioritizing synthetic images that appear realistic. However, our
empirical analysis shows that high-fidelity and visually appealing synthetic
images are not necessarily superior. In fact, we present a case where
low-fidelity synthetic images outperformed their high-fidelity counterparts in
downstream tasks. Our findings highlight the importance of comprehensive
analysis before incorporating synthetic data into real-world applications. We
hope our results will raise awareness among the research community of the value
of low-fidelity synthetic images in medical AI algorithm training.

研究表明：医疗人工智能算法训练所需的人工数据过于稀缺，使用深度生成模型生成的低保真度合成图像在某些情况下可能比高保真度合成图像更优，这为合成图像在现实应用中的价值提供了新思路。