Brainchop:为部署神经成像人工智能模型提供边缘生态系统
Sergey M Plis1, Mohamed Masoud1, Farfalla Hu1
1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, GA 30303, USA.
Aperture neuro
|September 20, 2024
概括
Brainchop是一个网络应用程序,允许用户将深度学习模型应用于浏览器中的神经成像数据. 这种开源工具加速了人工智能驱动的医学图像分析,如脑部提取和细分,使用本地硬件.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 深度学习模型在医学成像中显示出前景.
- 缺乏高效的分销平台限制了模型共享.
- 现有的解决方案通常依赖于基于云的处理,这引发了隐私问题.
研究的目的:
- 介绍Brainchop,一个用于将深度学习模型应用于神经成像数据的Web应用程序.
- 为医疗图像的高效和私有本地处理提供一个平台.
- 促进人工智能模型在各种神经成像任务中的分发和使用.
主要方法:
- 使用纯 JavaScript 开发了一个功能齐全的 Web 应用程序.
- 集成优化的辅助功能,用于体积合规和组件过.
- 利用终端用户的显卡来快速推断模型.
- 包括用于验证,注释和编辑的集成可视化工具.
主要成果:
- 脑可以快速 (几秒钟) 提取大脑,组织细分和区域分片.
- 该应用程序通过在用户本地机器上进行处理来确保数据隐私.
- 促进模型的分发,用于诸如注册和异常组织 (瘤,病变) 的识别等任务.
结论:
- Brainchop提供了一种高效的基于浏览器的解决方案,用于将深度学习应用于神经成像.
- 该平台增强了人工智能驱动的医学图像分析的可访问性和隐私.
- 通过其开源性质,鼓励在神经科学研究中更广泛地采用和开发AI模型.
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