岛:一个智能流媒体框架,用于医疗成像中的高通量AI推理
Pranav Kulkarni1, Adway Kanhere1, Eliot L Siegel1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, 100 N Greene St, Baltimore, MD, 21201, USA.
智能流媒体框架ISLE减少了放射学AI的带宽和计算需求. 这优化了AI推断,显著提高了处理速度,而不会影响诊断准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能在放射学领域的日益普及,增加了对带宽和计算资源的需求.
- 高昂的基础设施成本对医疗保健提供者和人工智能供应商来说是一个挑战.
- 当前的成像基础设施在支持人工智能部署方面面临效率低下的问题.
研究的目的:
- 开发一个智能流媒体框架,ISLE,以解决医疗成像基础设施的低效性.
- 为了减少AI推断在放射学中的带宽和计算要求.
- 提高人工智能系统处理医疗图像的吞吐量.
主要方法:
- 开发了ISLE,这是一个智能流媒体框架,灵感来自视频按需平台.
- 智能流传医疗图像以最佳分辨率用于AI推断,使用渐进编码.
- 通过传输胸部X射线进行分类和腹部CT扫描进行细分来评估ISLE.
主要成果:
- 在所有任务中,ISLE至少减少了82%的数据传输和解码时间.
- 吞吐量增加了超过2.9×的细分和3.72×的分类.
- 在所有评估任务中,AI诊断性能没有受到影响 (P>0.05).
结论:
- ISLE有效地解决了人工智能部署的成像基础设施效率低下问题.
- 该框架提高了临床AI应用中的数据和计算效率.
- 在不影响临床决策的情况下,ISLE可实现高效的AI部署.
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