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.

MIST 是一个开源框架，可以从一个高分辨率复制品流式传输医学图像，并在多个分辨率下进行操作，能够显著减少图像基础设施的低效性，同时对于深度学习应用程序也能够保持诊断质量。

一份副本足矣：规模化的医学影像数据高效流式传输

One Copy Is All You Need: Resource-Efficient Streaming of Medical  Imaging Data at Scale

We present a method to generate speech from input text and a style vector
that is extracted from a reference speech signal in an unsupervised manner,
i.e., no style annotation, such as speaker information, is required. Existing
unsupervised methods, during training, generate speech by computing style from
the corresponding ground truth sample and use a decoder to combine the style
vector with the input text. Training the model in such a way leaks content
information into the style vector. The decoder can use the leaked content and
ignore some of the input text to minimize the reconstruction loss. At inference
time, when the reference speech does not match the content input, the output
may not contain all of the content of the input text. We refer to this problem
as "content leakage", which we address by explicitly estimating and minimizing
the mutual information between the style and the content through an adversarial
training formulation. We call our method MIST - Mutual Information based Style
Content Separation. The main goal of the method is to preserve the input
content in the synthesized speech signal, which we measure by the word error
rate (WER) and show substantial improvements over state-of-the-art unsupervised
speech synthesis methods.

该研究提出了一种基于无监督学习的语音合成方法 MIST，旨在解决现有无监督方法在训练过程中出现 content leakage 的问题，通过显式估计和最小化样式与内容之间的互信息，达到保留输入内容的目的。研究结果表明，该方法在语音合成中相较于现有无监督方法表现更加优越。