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培训后网络压缩用于3D医疗图像细分:通过塔克分解减少计算力度

Tobias Weber¹, Jakob Dexl¹, David Rügamer¹

¹From the Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15, 81377 Munich, Germany (T.W., J.D., M.I.); Department of Statistics, LMU Munich, Munich, Germany (T.W., D.R.); and Munich Center for Machine Learning, Munich, Germany (T.W., J.D., D.R., M.I.).

Radiology. Artificial intelligence

|January 15, 2025

概括

使用塔克分解的网络压缩显著降低了使用TotalSegmentator进行3D CT细分的计算需求. 这种深度学习方法实现了实质性的参数减少,对细分精度的影响最小.

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:

准确的基于3DCT的多器官细分对于临床应用至关重要.
像TotalSegmentator这样的深度学习模型提供自动细分,但需要大量的计算资源.
研究网络压缩技术以提高效率.

研究的目的:

为了评估压缩TotalSegmentator模型的塔克尔分解.
在推理过程中减少计算力度 (浮点运算,内存).
评估压缩,推断速度和细分精度之间的权衡.

主要方法:

应用了Tucker分解到TotalSegmentator (nnU-Net) 模型的卷积内核.
使用了一个大型CT数据集 (1228次扫描) 和一个测试子集 (89次扫描).
通过使用Dice在各种下方采样因素中的得分来评估细分性能.

主要成果:

在模型参数 (高达88.17%) 和浮点运算方面实现了显著的减少.
在细分准确度中显示最小损失,在微调后117个类中的113个类中没有显著差异.
在GPU架构上观察到各种实用好处,在不那么强大的硬件上加快速度.

结论:

通过塔克分解的特点后网络压缩是医疗图像细分的可行策略.
降低了深度学习模型的计算需求,而不会对准确性产生重大影响.
能够更有效地部署先进的细分工具.

关键词:

卷积卷积是指卷积的过程.深度学习 (Deep Learning) 是一种深度学习.网络压缩网络压缩分段化分段化分段化分段化塔克尔分解法塔克尔分解法

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