一个极为轻量级的CNN模型用于在资源有限的环境中诊断胸部X射线图
Gautam Kumar1, Nirbhay Sharma1, Angshuman Paul1
1Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India.
Medical physics
|September 4, 2023
概括
一种新的,极为轻量级的卷积神经网络 (CNN) 模型,ExLNet,已被开发用于胸部X射线诊断. 这种计算效率高的模型提供了高性能,使先进的人工智能在资源有限的环境中可访问.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 对于胸部X射线诊断的深度学习模型是计算密集的.
- 高计算需求限制了在资源有限的环境中部署先进的人工智能.
- 需要轻量级的深度学习模型来弥合这一差距.
研究的目的:
- 开发一个计算轻量级模型用于胸部X光学诊断.
- 该模型具有最小的参数 (0.14M) 和大小 (550 KB),用于在低资源环境中部署.
主要方法:
- 设计了一种新的架构,ExLNet (极轻型卷积神经网络).
- 该架构使用深度卷积在挤压和扩展 (DCISE) 块.
- DCISE块融合深度卷曲与挤压和扩展操作以减少计算负载.
主要成果:
- ExLNet在NIH,VinBig和Chexpert数据集上进行了严格的测试,用于二进制和多类分类.
- 该模型在未见的数据上实现了与最先进的方法相比更高的性能.
- 尽管参数数量大大减少,但ExLNet表现出强的结果.
结论:
- ExLNet提供了一个有效的轻量级CNN架构,用于胸部X射线分类.
- 新的DCISE块显著降低了计算负担.
- 该模型的效率和一致性能使其适用于资源有限的临床环境.
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