One Copy Is All You Need: Resource-Efficient Streaming of Medical
  Imaging Data at Scale
Pranav Kulkarni Adway Kanhere Eliot Siegel Paul H. Yi Vishwa S. Parekh
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.