Large-scale medical imaging datasets have accelerated development of artificial intelligence tools for clinical decision support. However, the large size of these datasets is a bottleneck for users with limited storage and bandwidth. Many users may not even require such large datasets as AI models are often trained on lower resolution images. If users could directly download at their desired resolution, storage and bandwidth requirements would significantly decrease. However, it is impossible to anticipate every users' requirements and impractical to store the data at multiple resolutions. What if we could store images at a single resolution but send them at different ones? We propose MIST, an open-source framework to operationalize progressive resolution for streaming medical images at multiple resolutions from a single high-resolution copy. We demonstrate that MIST can dramatically reduce imaging infrastructure inefficiencies for hosting and streaming medical images by >90%, while maintaining diagnostic quality for deep learning applications.

翻译：大规模医学影像数据集加速了临床决策支持人工智能工具的开发。然而，这些数据集的大体量对存储和带宽有限的用户构成了瓶颈。许多用户可能并不需要如此庞大的数据集，因为人工智能模型通常基于较低分辨率的图像进行训练。若用户能直接按所需分辨率下载，存储和带宽需求将显著降低。但预判每位用户的需求几乎不可能，且以多种分辨率存储数据又缺乏实践性。我们能否实现仅存储单一分辨率图像，却按不同分辨率传输？为此，我们提出MIST——一个开源框架，通过渐进式分辨率技术，基于单一高分辨率副本实现医学图像的多分辨率流式传输。实验表明，MIST可将托管与流式传输医学图像的成像基础设施效率提升超过90%，同时保持深度学习应用所需的诊断质量。